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• Extract depressive symptoms from writings written by patients. • Enables the identification and visualization of depressive symptoms using a deep attention based method. • The framework acts as a decision support system as it incorporates entropy based labeling of data. • Our online interactive tool (ICT) helps provide context as well as visualization of detected depressive symptoms. • Evaluated different architecture design for the attention mechanism combined with fuzzy rules. In this paper, we propose a structure hypergraph and an emotional lexicon for word representation. Our method can solve problems related to vocabulary size, grammatical representation of words, and the lack of an emotional lexicon. Natural Language Processing (NLP) and attention-based curriculum learning are then used in the developed model. The goal is to achieve semantic word representations using a graph model. Later, embedding is used to label the text using clinical procedures. The experimental results show the emotional word representation with the structure hypergraph. The bidirectional Long Short Term Memory (LSTM) architecture with an attention mechanism achieved a Receiver Operating Characteristic (ROC) value of 0.96. The learning method can help psychiatrists in note taking and contributes to the detection rate of depression symptoms. [ABSTRACT FROM AUTHOR]

High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades since it reveals profit strategies for use in industry for decision-making. Most existing works have focused on mining high-utility itemsets from databases showing large amount of patterns; however exact decisions are still challenging to make from that large amounts of discovered knowledge. Closed High-utility itemset mining (CHUIM) provides a smart way to present concise high-utility itemsets that can be more effective for making correct decisions. However, none of the existing works have focused on handling large-scale databases to integrate discovered knowledge from several distributed databases. In this paper, we first present a large-scale information fusion architecture to integrate discovered closed high-utility patterns from several distributed databases. The generic composite model is used to cluster transactions regarding their relevant correlation that can ensure correctness and completeness of the fusion model. The well-known MapReduce framework is then deployed in the developed DFM-Miner algorithm to handle big datasets for information fusion and integration. Experiments are then compared to the state-of-the-art CHUI-Miner and CLS-Miner algorithms for mining closed high-utility patterns and the results indicated that the designed model is well designed for handling large-scale databases with less memory usage. Moreover, the designed MapReduce framework can speed up the mining performance of closed high-utility patterns in the developed fusion system. • Mine required CHUIs in parallel and distributed environments. • Use HG-k-means to cluster highly relevant transactions in a group that ensures fusion. • An interactive three-phases MapReduce model with a DFM-Miner algorithm is presented. • Experiments on various large-scale datasets are conducted to achieve better performance. [ABSTRACT FROM AUTHOR]

• CRFLV-I and CRFLV-II are proposed for sequence labeling. • Encoding schema is administered in the form of latent variables able to capture structures of hidden variables. • The proposed models make use of different schemas in different scenarios. • We show that our schemas holds strong performance against BIO or BILOU encoding schemas. To process data like text and speech, Natural Language Processing (NLP) is a valuable tool. As on of NLP's upstream tasks, sequence labeling is a vital part of NLP through techniques like text classification, machine translation, and sentiment analysis. In this paper, our focus is on sequence labeling where we assign semantic labels within input sequences. We present two novel frameworks, namely SA-CRFLV-I and SA-CRFLV-II, that use latent variables within random fields. These frameworks make use of an encoding schema in the form of a latent variable to be able to capture the latent structure in the observed data. SA-CRFLV-I shows the best performance at the sentence level whereas SA-CRFLV-II works best at the word level. In our in-depth experimental results, we compare our frameworks with 4 well-known sequence prediction methodologies which include NER, reference parsing, chunking as well as POS tagging. The proposed frameworks are shown to have better performance in terms of many well-known metrics. [ABSTRACT FROM AUTHOR]

Natural language processing is a useful processing technique of language data, such as text and speech. Sequence labeling represents the upstream task of many natural language processing tasks, such as machine translation, text classification, and sentiment classification. In this paper, the focus is on the sequence labeling task, in which semantic labels are assigned to each unit of a given input sequence. Two frameworks of latent variable conditional random fields (CRF) models (called LVCRF-I and LVCRF-II) are proposed, which use the encoding schema as a latent variable to capture the latent structure of the hidden variables and the observed data. Among the two designed models, the LVCRF-I model focuses on the sentence level, while the LVCRF-II works in the word level, to choose the best encoding schema for a given input sequence automatically without handcraft features. In the experiments, the two proposed models are verified by four sequence prediction tasks, including named entity recognition (NER), chunking, reference parsing and POS tagging. The proposed frameworks achieve better performance without using other handcraft features than the conventional CRF model. Moreover, these designed frameworks can be viewed as a substitution of the conventional CRF models. In the commonly used LSTM-CRF models, the CRF layer can be replaced with our proposed framework as they use the same training and inference procedure. The experimental results show that the proposed models exhibit latent variable and provide competitive and robust performance on all three sequence prediction tasks. [ABSTRACT FROM AUTHOR]

Natural language processing is a technique to process data such as text and speech. Some fundamental research includes named-entity recognition, which recognizes name entities (i.e., persons, companies) from texts; semantic parsing, which is used to convert a natural language utterance to the representation of logical form; and co-reference resolution, which extracts nouns (including pronouns, noun phrases) pointing to the same reference body. In this paper, we mainly focus on the task of mention extraction, which extract and classify overlapping or nested structure mentions. We proposed a neural-encoded mention-hypergraph (NEMH) model to use hypergraph to model overlapping or nested structure mentions and use neural networks to extract features for hypergraph automatically. Unlike the existing approaches, our hypergraph model can effectively capture nested mention entities with unlimited lengths. Also, the proposed model is highly scalable and the time complexity of the proposed model is linear in the number of mention classes and the number of input words. Extensive experiments are conducted on several standard datasets to demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Abstract Association-rule mining (ARM) or frequent itemset mining (FIM) is the most fundamental task in knowledge discovery, which is used to find the occurrence frequency of the item/sets in transactional database. The other factors such as weight, interestingness or unit profit of the items are not considered whether in ARM or FIM. To reveal more information, high-utility itemset mining (HUIM) was designed to consider both quantity and unit profit of items to discover the high-utility itemsets (HUIs). Several algorithms for FIM or HUIM were extensively studied but fewer works concern both frequency and utility together to provide better solutions in decision-making. In the past, the SKYMINE algorithm was designed to find the skyline frequent-utility patterns (SFUPs). A SFUP is a non-dominated pattern, in which each solution dominates the others by considering the aspects of frequency and utility. The SKYMINE algorithm needs, however, amounts of computation to level-wisely discover the SFUPs. In this paper, an efficient utility-list structure is used instead of the UP-tree structure used in SKYMINE to mine the SFUPs. Two algorithms are respectively designed by using the depth-first search (called SKYFUP-D) and breath-first search (SKYFUP-B) to mine the SFUPs. An efficient structure is also designed to record the maximal utility of the potential itemsets, thus reducing the computations for finding the SFUPs in the search space. Extensive experiments are conducted on several real-world and simulated datasets and the results indicate that the designed two algorithms have better performance than that of the state-of-the-art SKYMINE algorithm in terms of runtime, memory usage, search space size and the scalability. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is an extension of frequent-itemset mining (FIM) but considers the unit profit and quantity of items to discover the set of high-utility itemsets (HUIs). Traditionally, the utility of an itemset is the summation of the utilities of the itemset in all the transactions regardless of its length. This approach is, however, inappropriate in real-world applications since the utility of the itemset increases along with the number of items within it. High average-utility itemset mining (HAUIM) was designed to provide more reasonable utility measure by taking the size of the itemset into account. Existing algorithms can only handle, however, the static database and unsuitable for the dynamic environment since the size of data is frequently changed in real-life situations. In this paper, an incremental high-average utility pattern mining (IHAUPM) algorithm is presented to handle the incremental database with transaction insertion. The well-known fast updated (FUP) concept in the FIM is modified to adopt the designed algorithm, thus efficiently updating the discovered HAUIs. Based on the designed model for HAUIM with transaction insertion, the proposed IHAUPM algorithm can easily only handle the inserted transactions. Experiments are carried on six datasets and the results showed that the designed algorithm has better performance than the state-of-the-art algorithms performing in the batch manner. [ABSTRACT FROM AUTHOR]

From web search and data mining, users’ click and purchase behaviors contain valuable information, thus numerous approaches have been proposed to identify embedded useful knowledge from them. In these real-life situations, each user may perform the same action/event multiple times, and multiple accessed events product different profit. Many utility-oriented data mining approaches thus have been extensively studied. Previous studies have the limitation that the overall utility of traditional pattern is limited since they rarely consider the inherent correlation. For example, from the purchase behavior, the low-utility patterns sometimes with a very high-utility pattern will be considered as a valuable pattern even if this behavior may be not highly correlated. A more intelligent framework that provides non-redundant and correlated behavior based on utility measure is thus desired. In this paper, we first present a novel method to extract non-redundant correlated purchase behaviors considering the utility and correlation factors. The high qualified patterns can be derived with high profit and strong correlation, which can lead to higher recall and reveal better precision. In the proposed projection-based approach, an efficient projection mechanism and a sorted downward closure property are developed to reduce the database size. Several pruning strategies are further developed to efficiently and effectively discover the desired patterns. An extensive experimental study showed that the novel non-redundant correlated high-utility pattern has more effectiveness than the previous knowledge representation. Moreover, the proposed algorithm is efficient in terms of execution time and memory usage. [ABSTRACT FROM AUTHOR]

The discovery of high-utility itemsets (HUIs) in transactional databases has attracted much interest from researchers in recent years since it can uncover hidden information that is useful for decision making, and it is widely used in many domains. Nonetheless, traditional methods for high-utility itemset mining (HUIM) utilize the utility measure as sole criterion to determine which item/sets should be presented to the user. These methods ignore the timestamps of transactions and do not consider the period constraint. Hence, these algorithms often finds HUIs that are profitable but that seldom occur in transactions. In this paper, we address this limitation of previous methods by pushing the period constraint in the HUI mining process. A new framework called short-period high-utility itemset mining (SPHUIM) is designed to identify patterns in a transactional database that appear regularly, are profitable, and also yield a high utility under the period constraint. The aim of discovering short-period high-utility itemsets (SPHUI) is hence to identify patterns that are interesting both in terms of period and utility. The paper proposes a baseline two-phase short-period high-utility itemset (SPHUI T P ) mining algorithm to mine SPHUIs in a level-wise manner. Then, to reduce the search space of the SPHUI TP algorithm and speed up the discovery of SPHUIs, two pruning strategies are developed and integrated in the baseline algorithm. The resulting algorithms are denoted as SPHUI MT and SPHUI TID , respectively. Substantial experiments both on real-life and synthetic datasets show that the three proposed algorithms can efficiently and effectively discover the complete set of SPHUIs, and that considering the short-period constraint and the utility measure can greatly reduce the number of patterns found. [ABSTRACT FROM AUTHOR]

Weighted Frequent Itemset Mining (WFIM) has been proposed as an extension of frequent itemset mining that considers not only the frequency of items but also their relative importance. However, using WFIM algorithms in real applications raises some problems. First, they do not consider how recent the patterns are. Second, traditional WFIM algorithms cannot handle uncertain data, although this type of data is common in real-life. To address these limitations, this paper introduces the concept of Recent High Expected Weighted Itemset (RHEWI), which considers the recency, weight and uncertainty of patterns. By considering these three factors, more up-to-date and relevant results are found. A projection-based algorithm named RHEWI-P is presented to mine RHEWIs using a novel upper - bound downward closure ( UBDC ) property. An improved version of this algorithm called RHEWI-PS is further proposed based on a novel sorted upper-bound downward closure ( SUBDC ) property for pruning unpromising candidate itemsets early. An experimental evaluation against the state-of-the-art HEWI-Uapriori algorithm was carried out on both real-world and synthetic datasets. Results show that the proposed algorithms are highly efficient and are acceptable for mining the desired patterns. [ABSTRACT FROM AUTHOR]

Frequent pattern mining (FPM) is an important topic in data mining for discovering the implicit but useful information. Many algorithms have been proposed for this task but most of them suffer from an important limitation, which relies on a single uniform minimum support threshold as the sole criterion to identify frequent patterns (FPs). Using a single threshold value to assess the usefulness of all items in a database is inadequate and unfair in real-life applications since each item is different and not all items should be treated as the same. Several algorithms have been developed for mining FPs with multiple minimum supports but most of them suffer from the time-consuming problem and require a large amount of memory. In this paper, we address this issue by introducing the novel approach named F requent P attern mining with M ultiple minimum supports from the E numeration-tree (FP-ME). In the developed Set- E numeration -tree with M ultiple minimum supports (ME-tree) structure, a new sorted downward closure ( SDC ) property of FPs and the least minimum support ( LMS ) concept with multiple minimum supports are used to effectively prune the search space. The proposed FP-ME algorithm can directly discover FPs from the ME-tree without candidate generation. Moreover, an improved algorithm, named FP-ME DiffSet , is also developed based on the DiffSet concept, to further increase mining performance. Substantial experiments on both real-life and synthetic datasets show that the proposed algorithms can not only avoid the “ rare item problem ”, but also efficiently and effectively discover the complete set of FPs in transactional databases while considering multiple minimum supports and outperform the state-of-the-art CFP-growth++ algorithm in terms of execution time, memory usage and scalability. [ABSTRACT FROM AUTHOR]

In the field of data mining, the topic of high-utility itemset mining (HUIM) has recently gained a lot of attention from researchers as it takes many factors into account that are useful for decision-making by retail managers. In the past, many algorithms have been presented for HUIM but most of them suffer from the limitation of using a single minimum utility threshold to identify high-utility itemsets (HUIs). For real-life applications, finding itemsets using a single threshold is inadequate and unfair since each item is different. Hence, the diversity or importance of each item should be considered. This paper proposes a solution to this issue by defining the novel task of HUIM with multiple minimum utility thresholds (named as HUIM-MMU). This task lets users specify a different minimum utility threshold for each item to identify more useful and specific HUIs, which would generate more profits when compared to HUIs discovered based on a single minimum utility threshold. The HUI-MMU algorithm is designed to mine HUIs in a level-wise manner. The sorted downward closure ( SDC ) property and the least minimum utility ( LMU ) concept are developed to avoid a combinatorial explosion for identifying HUIs and to ensure the completeness and correctness of HUI-MMU for discovering HUIs. Meanwhile, two improved algorithms, namely HUI-MMU TID and HUI-MMU TE , are presented based on the TID-index and EUCP strategies. Those strategies can be used to speed up the mining performance to discover HUIs. Substantial experiments on both real-life and synthetic datasets show that the designed algorithms can efficiently and effectively discover the complete set of HUIs in databases by considering multiple minimum utility thresholds. [ABSTRACT FROM AUTHOR]

High utility itemset mining is an emerging data mining task, which consists of discovering highly profitable itemsets (called high utility itemsets) in very large transactional databases. Many algorithms have been proposed to efficiently discover high utility itemsets but most of them assume that items may only have positive unit profits. However, in real-world transactional databases, items (products) often have positive or negative unit profits. Mining high utility itemsets in a transactional database where items have positive or negative unit profits is a computationally expensive task, and it is thus desirable to design more efficient algorithms. To address this issue, we propose an efficient algorithm named FHN (Faster High-Utility itemset miner with Negative unit profits). It relies on a novel PNU-list structure (Positive-and-Negative Utility-list) structure to efficiently mine high utility itemsets, while considering both positive and negative unit profits. Moreover, several pruning strategies are introduced in FHN to reduce the number of candidate itemsets, and thus enhance the performance of FHN. Extensive experimental results on both real-life and synthetic datasets show that the proposed FHN algorithm is in general two to three orders of magnitude faster and can use up to 200 times less memory than the state-of-the-art algorithm HUINIV-Mine. Moreover, it is shown that FHN performs especially well on dense datasets. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of the designed algorithms have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large. In the past, a heuristic HUPE umu -GRAM algorithm was proposed to mine HUIs based on genetic algorithm (GA). For the evolutionary computation (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approach. Since the traditional PSO mechanism is used to handle the continuous problem, in this paper, the discrete PSO is adopted to encode the particles as the binary variables. An efficient PSO-based algorithm namely HUIM-BPSO sig is proposed to efficiently find HUIs. It first sets the number of discovered high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of a particle based on transaction-weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process. The sigmoid function is adopted in the updating process of the particles of the designed HUIM-BPSO sig algorithm. Substantial experiments on real-life datasets show that the proposed algorithm has better results compared to the state-of-the-art GA-based algorithm. [ABSTRACT FROM AUTHOR]

High-Utility Itemset Mining (HUIM) is an extension of frequent itemset mining, which discovers itemsets yielding a high profit in transaction databases (HUIs). In recent years, a major issue that has arisen is that data publicly published or shared by organizations may lead to privacy threats since sensitive or confidential information may be uncovered by data mining techniques. To address this issue, techniques for privacy-preserving data mining (PPDM) have been proposed. Recently, privacy-preserving utility mining (PPUM) has become an important topic in PPDM. PPUM is the process of hiding sensitive HUIs (SHUIs) appearing in a database, such that the resulting sanitized database will not reveal these itemsets. In the past, the HHUIF and MSICF algorithms were proposed to hide SHUIs, and are the state-of-the-art approaches for PPUM. In this paper, two novel algorithms, namely Maximum Sensitive Utility-MAximum item Utility (MSU-MAU) and Maximum Sensitive Utility-MInimum item Utility (MSU-MIU), are respectively proposed to minimize the side effects of the sanitization process for hiding SHUIs. The proposed algorithms are designed to efficiently delete SHUIs or decrease their utilities using the concepts of maximum and minimum utility. A projection mechanism is also adopted in the two designed algorithms to speed up the sanitization process. Besides, since the evaluation criteria proposed for PPDM are insufficient and inappropriate for evaluating the sanitization performed by PPUM algorithms, this paper introduces three similarity measures to respectively assess the database structure, database utility and item utility of a sanitized database. These criteria are proposed as a new evaluation standard for PPUM. [ABSTRACT FROM AUTHOR]

Privacy-preserving data mining (PPDM) has become an important research field in recent years, as approaches for PPDM can discover important information in databases, while ensuring that sensitive information is not revealed. Several algorithms have been proposed to hide sensitive information in databases. They apply addition and deletion operations to perturb an original database and hide the sensitive information. Finding an appropriate set of transactions/itemsets to be perturbed for hiding sensitive information while preserving other important information is a NP-hard problem. In the past, genetic algorithm (GA)-based approaches were developed to hide sensitive itemsets in an original database through transaction deletion. In this paper, a particle swarm optimization (PSO)-based algorithm called PSO2DT is developed to hide sensitive itemsets while minimizing the side effects of the sanitization process. Each particle in the designed PSO2DT algorithm represents a set of transactions to be deleted. Particles are evaluated using a fitness function that is designed to minimize the side effects of sanitization. The proposed algorithm can also determine the maximum number of transactions to be deleted for efficiently hiding sensitive itemsets, unlike the state-of-the-art GA-based approaches. Besides, an important strength of the proposed approach is that few parameters need to be set, and it can still find better solutions to the sanitization problem than GA-based approaches. Furthermore, the pre-large concept is also adopted in the designed algorithm to speed up the evolution process. Substantial experiments on both real-world and synthetic datasets show that the proposed PSO2DT algorithm performs better than the Greedy algorithm and GA-based algorithms in terms of runtime, fail to be hidden (F-T-H), not to be hidden (N-T-H), and database similarity (DS). [ABSTRACT FROM AUTHOR]

With the ever increasing number of applications of data mining, high-utility itemset mining (HUIM) has become a critical issue in recent decades. In traditional HUIM, the utility of an itemset is defined as the sum of the utilities of its items, in transactions where it appears. An important problem with this definition is that it does not take itemset length into account. Because the utility of larger itemset is generally greater than the utility of smaller itemset, traditional HUIM algorithms tend to be biased toward finding a set of large itemsets. Thus, this definition is not a fair measurement of utility. To provide a better assessment of each itemset’s utility, the task of high average-utility itemset mining (HAUIM) was proposed. It introduces the average utility measure, which considers both the length of itemsets and their utilities, and is thus more appropriate in real-world situations. Several algorithms have been designed for this task. They can be generally categorized as either level-wise or pattern-growth approaches. Both of them require, however, the amount of computation to find the actual high average-utility itemsets (HAUIs). In this paper, we present an efficient average-utility (AU)-list structure to discover the HAUIs more efficiently. A depth-first search algorithm named HAUI-Miner is proposed to explore the search space without candidate generation, and an efficient pruning strategy is developed to reduce the search space and speed up the mining process. Extensive experiments are conducted to compare the performance of HAUI-Miner with the state-of-the-art HAUIM algorithms in terms of runtime, number of determining nodes, memory usage and scalability. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining high-utility itemsets (HUIs) assume that the information stored in databases is precise, i.e., that there is no uncertainty. But in many real-life applications, an item or itemset is not only present or absent in transactions but is also associated with an existence probability. This is especially the case for data collected experimentally or using noisy sensors. In the past, many algorithms were respectively proposed to effectively mine frequent itemsets in uncertain databases. But mining HUIs in an uncertain database has not yet been proposed, although uncertainty is commonly seen in real-world applications. In this paper, a novel framework, named potential high-utility itemset mining (PHUIM) in uncertain databases, is proposed to efficiently discover not only the itemsets with high utilities but also the itemsets with high existence probabilities in an uncertain database based on the tuple uncertainty model. The PHUI-UP algorithm (potential high-utility itemsets upper-bound-based mining algorithm) is first presented to mine potential high-utility itemsets (PHUIs) using a level-wise search. Since PHUI-UP adopts a generate-and-test approach to mine PHUIs, it suffers from the problem of repeatedly scanning the database. To address this issue, a second algorithm named PHUI-List (potential high-utility itemsets PU-list-based mining algorithm) is also proposed. This latter directly mines PHUIs without generating candidates, thanks to a novel probability-utility-list (PU-list) structure, thus greatly improving the scalability of PHUI mining. Substantial experiments were conducted on both real-life and synthetic datasets to assess the performance of the two designed algorithms in terms of runtime, number of patterns, memory consumption, and scalability. [ABSTRACT FROM AUTHOR]

High-utility itemsets mining (HUIM) is a critical issue which concerns not only the occurrence frequencies of itemsets in association-rule mining (ARM), but also the factors of quantity and profit in real-life applications. Many algorithms have been developed to efficiently mine high-utility itemsets (HUIs) from a static database. Discovered HUIs may become invalid or new HUIs may arise when transactions are inserted, deleted or modified. Existing approaches are required to re-process the updated database and re-mine HUIs each time, as previously discovered HUIs are not maintained. Previously, a pre-large concept was proposed to efficiently maintain and update the discovered information in ARM, which cannot be directly applied into HUIM. In this paper, a maintenance (PRE-HUI-MOD) algorithm with transaction modification based on a new pre-large strategy is presented to efficiently maintain and update the discovered HUIs. When the transactions are consequentially modified from the original database, the discovered information is divided into three parts with nine cases. A specific procedure is then performed to maintain and update the discovered information for each case. Based on the designed PRE-HUI-MOD algorithm, it is unnecessary to rescan original database until the accumulative total utility of the modified transactions achieves the designed safety bound, which can greatly reduce the computations of multiple database scans when compared to the batch-mode approaches. [ABSTRACT FROM AUTHOR]

High-utility pattern mining (HUPM) is an emerging topic in recent years instead of association-rule mining to discover more interesting and useful information for decision making. Many algorithms have been developed to find high-utility patterns (HUPs) from quantitative databases without considering timestamp of patterns, especially in recent intervals. A pattern may not be a HUP in an entire database but may be a HUP in recent intervals. In this paper, a new concept namely up-to-date high-utility pattern (UDHUP) is designed. It considers not only utility measure but also timestamp factor to discover the recent HUPs. The UDHUP-apriori is first proposed to mine UDHUPs in a level-wise way. Since UDHUP-apriori uses Apriori-like approach to recursively derive UDHUPs, a second UDHUP-list algorithm is then presented to efficiently discover UDHUPs based on the developed UDU-list structures and a pruning strategy without candidate generation, thus speeding up the mining process. A flexible minimum-length strategy with two specific lifetimes is also designed to find more efficient UDHUPs based on a users’ specification. Experiments are conducted to evaluate the performance of the proposed two algorithms in terms of execution time, memory consumption, and number of generated UDHUPs in several real-world and synthetic datasets. [ABSTRACT FROM AUTHOR]

Ransomware continues to pose a significant threat to cybersecurity by extorting money from users by locking their devices and personal data. The attackers force the payment of a ransom in order to restore access to personal files. Because of the structural similarity, detection of ransomware and benign applications becomes vulnerable to evasion attacks. Ensemble learning can provide countermeasures, while attackers can use the same technique to improve the effectiveness of their respective attacks. This motivates us to investigate whether the distinct ensemble method can achieve better performance when combined with the voting-based method. This research proposes a hybrid approach that examines permissions, text, and network-based features both statically and dynamically by monitoring memory usage, system call logs, and CPU usage. Ensemble machine learning analyzers on static and dynamic features extracted from Android malware applications (ransomware and non-ransomware) are then trained in the designed model. Our experimental results show that the proposed ensemble classification and detection technique can classify unknown static and dynamic ransomware behavior to mitigate adversarial evasion attacks. [Display omitted] • The designed model extracts and analyzes static network-based features. • Two ML-based ensemble models are proposed for static and dynamic feature sets. • The model is for Android ransomware adversarial evasion attacks. [ABSTRACT FROM AUTHOR]

• An improved GA is presented for nutrient recommendation in time-series sensor data. • An exploration is used to speed evolution process and increase convergence rate. • An exploitation is used to optimize the parameters for achieving maximum yield. In agricultural production, soil characteristics play a vital role in maintaining fertility by allowing crops to develop better through root nutrition with minimal energy inputs. Nitrogen (N), Phosphorus (P), and Potassium (K) are all important nitrogen fertilizers extensively utilized in crops to supply a sufficient level of nutrients and keep their production level high. However, the application is generally limited to specific crops because of the global variability in these essential nutrients. Stability in fertilizer application, growth, and root growth rate increases crop fertility and crop production. To predict the suitable nutrients for different crops and provide nutrients recommendations by analyzing the crop fertility and yield production, this paper proposes nutrient recommendations through an improved genetic algorithm (IGA) that uses time-series sensor data and recommends various crop settings. A neighborhood-based strategy is then presented to handle exploration and exploitation for optimizing the parameters to obtain the maximum yield. The method can expand knowledge by using the population exploration strategy. The final recommendation is made by using the similarity between recommended patterns and real-time sensor data. With time, crop fertility decreases due to the low level of nutrients. This crop model will help to increase yield by analysis of the seasonal fertility performance of the soil. The proposed method is also a useful tool to improve soil fertility performance by providing the nutrient recommendation of optimal conditions for crop development. Experimental results show that the proposed model can recommend optimizing patterns and increasing the yearly yield efficiently. The method can help identify the region to assess crop suitability under certain nutrients levels and give insight into nutrient suitability assessments concerning specific crops in a climate-changing world. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) is useful for handling text and speech, and sequence labeling plays an important role by automatically analyzing a sequence (text) to assign category labels to each part. However, the performance of these conventional models depends greatly on hand-crafted features and task-specific knowledge, which is a time consuming task. Several conditional random fields (CRF)-based models for sequence labeling have been presented, but the major limitation is how to use neural networks for extracting useful representations for each unit or segment in the input sequence. In this paper, we propose an attention segmental recurrent neural network (ASRNN) that relies on a hierarchical attention neural semi-Markov conditional random fields (semi-CRF) model for the task of sequence labeling. Our model uses a hierarchical structure to incorporate character-level and word-level information and applies an attention mechanism to both levels. This enables our method to differentiate more important information from less important information when constructing the segmental representation. We evaluated our model on three sequence labeling tasks, including named entity recognition (NER), chunking, and reference parsing. Experimental results show that the proposed model benefited from the hierarchical structure, and it achieved competitive and robust performance on all three sequence labeling tasks. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is a major contemporary data mining issue. It is different from frequent itemset mining (FIM), which only considers the frequency factor. HUIM applies both the quantity and profit factors to be used to reveal the most profitable products. Several previous approaches have been proposed to mine high-utility itemsets (HUIs) and most of them have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of the database are both very large. Therefore, two evolutionary computation (EC) techniques, genetic algorithm (GA) and particle swarm optimization (PSO), were previously proposed to mine HUIs. In these studies, GAs and PSOs also could obtain the huge amount of high-utility items in a limitation time. In this paper, a novel algorithm based on the other evolutionary computation technique, ant colony optimization (ACO), is proposed to resolve this issue. Unlike GAs and PSOs, ACOs produce a feasible solution in a constructive way. They can avoid generating unreasonable solutions as much as possible. Thus, a well-defined ACO approach can always obtain suitable solutions efficiently. An ant colony system (ACS), which is extended from ACO and consists of high-utility itemset mining by ACS (HUIM-ACS), is proposed to efficiently find HUIs. In general, an EC algorithm cannot make sure the provided solution is the global optimal solution. But the designed HUIM-ACS algorithm maps the completed solution space into the routing graph and includes two pruning processes. Therefore, it guarantees that it obtains all of the HUIs when there is no candidate edge from the starting point. In addition, HUIM-ACS does not estimate the same feasible solution again in its process in order to avoid wasting computational resource. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of the number of discovered HUIs, and convergence. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM), especially vanilla LSTM (VLSTM), has been widely used in air quality prediction field. However, VLSTM has many more parameters, thereby making training slow and prediction performance unstable. The VLSTM network input data have not been selected for better efficiency. In this paper, we propose an air quality prediction model based on the improved VLSTM with multichannel input and multiroute output (IVLSTM-MCMR). The proposed model includes the IVLSTM and MCMR modules. The proposed IVLSTM module is developed by improving the VLSTM inner structure of VLSTM in order to reduce the number of parameters that help to accelerate the convergence. A new historical information usage approach is further proposed to obtain a stable training process. For the MCMR module, a multichannel data input model (MC) with an improved linear similarity dynamic time warping is introduced to choose the valid data as the input of IVLSTM. A multiroute output model (MR) is designed to integrate the results from MC, in which the results of different target stations with different features are output by different routes. We evaluate the proposed model with the collected data from Beijing, China, and the experimental results show that our model achieves improvements regarding the predication performance. • An air quality prediction model IVLSTM-MCMR is proposed based on deep learning. • The number of parameters in IVLSTM-MCMR is reduced to accelerate the convergence. • The improved linear similarity dynamic time warping is introduced in IVLSTM-MCMR. • The integration of multi-channel data input and multi-route output is designed. [ABSTRACT FROM AUTHOR]

• We propose a Transformer-based network architecture for video salient object detection. • We propose a Gated Cross Reference (GCR) module to facilitate collaborative learning between appearance and motion cues. • The proposed Transformer-based method reaches excellent performance on five public benchmarks. Video salient object detection is a fundamental computer vision task aimed at highlighting the most conspicuous objects in a video sequence. There are two key challenges presented in video salient object detection: (1) how to extract effective feature representations from appearance and motion cues, and (2) how to combine both of them into robust saliency representation. To handle these challenges, in this paper, we propose a novel Transformer-based Cross Reference Network (TCRN), which fully exploits long-range context dependencies in both feature representation extraction and cross-modal (i.e., appearance and motion) integration. In contrast to existing CNN-based methods, our approach formulates video salient object detection as a sequence-to-sequence prediction task. In the proposed approach, the deep feature extraction is achieved by a pure vision transformer with multi-resolution token representations. Specifically, we design a Gated Cross Reference (GCR) module to effectively integrate appearance and motion into saliency representation. The GCR first propagates global context information between different modalities, and then perform cross-modal fusion by a gate mechanism. Extensive evaluations on five widely-used benchmarks show that the proposed Transformer-based method performs favorably against the existing state-of-the-art methods [ABSTRACT FROM AUTHOR]

It is uncertain whether products will be manufactured as defective products. Producing the defective products causes the waste of time and finance in the industrial fields. When the financial crisis occurs in the manufacturing plants, production lines of the less profitable products should be stopped. To overcome the financial crisis, it is required to find the production lines that manufacture the less profitable commodities and are likely to produce the faulty merchandises and remove the corresponding production lines in order to increase the profit. Erasable pattern mining has been suggested to analyze and solve the financial problems by discovering patterns with low profit. As databases become more diverse due to the environment and characteristics of data, demands for erasable pattern mining that processes the various databases increase. An uncertain database, which is one of diverse types of databases, includes the probability related to the item, such as the probability of existence and the probability of defects. Erasable pattern mining, which considers the probability of each item, can discover pattern results which are more suitable for purpose of users. Motivated by this, we propose an efficient tree-based erasable pattern mining algorithm from the uncertain database. Considering the probability of the item, the suggested algorithm extracts erasable patterns with low profits from the uncertain database. Since both profit of the product and the probability of the item are considered, the discovered patterns are more meaningful than the patterns found by the existing erasable pattern mining. Moreover, the proposed method improves the mining operations by utilizing a list structure as well as a tree structure. We performed a performance evaluation on diverse real and synthetic datasets in order to prove the proposed algorithm outperforms the existing erasable pattern mining algorithms. The differences in runtime, memory usage, and scalability are shown through the comparison of performance with other state-of-the-art algorithms and the suggested algorithm. [ABSTRACT FROM AUTHOR]

This paper discusses the fault-tolerant consensus problem in Unmanned Aerial Vehicle Networks (UAVNets). In recent years, the applications of UAVNets have become more and more popular. Related applications include aerial photography, geological and topographic surveys, disaster monitoring, military applications, and so on. Therefore, it is very important to build a highly reliable and fault-tolerant UAVNets. However, the network architecture of UAVNets is very different from previous network architectures. Because UAVs fly at a high speed, the topology also varies quickly. Hence, how to collect sufficient messages to reach a consensus on the network for UAVs is a challenge. In this paper, the characteristics of the distributed UAVNets will be explored first. Then, based on the characteristics of the UAVNets, a new fault-tolerant consensus protocol called the UAV Consensus Protocol (UCP) is proposed. The proposed UCP consists of two phases: the message exchanging phase and the consensus making phase. The proposed UCP can solve the consensus problem with ⌞(n O -1)/3⌟+1 rounds of message exchange in the presence of ⌞(n O -1- a O)/3⌟ Byzantine faulty UAVs and a O away UAVs, where n O is the number of AUVs in the UAVNet. Moreover, the correctness of the proposed UCP is also proved in this paper. [ABSTRACT FROM AUTHOR]

Opening range breakout (ORB) is a well-known intraday trading strategy via technical analysis. ORB lacks robustness against market uncertainties (e.g., information from contradictory sources), and does not consider all relevant market characteristics. Furthermore, the closing strategies in generic ORB are not well defined. In this study, we developed an evolutionary ORB-based model, which utilized historical data to optimize thresholds in order to enhance profitability, and developed protective closing strategies aimed at to prevent unacceptable losses. Selecting appropriate thresholds and parameters for ORB is a non-trivial task, due to the fact that the search space exceeds sixty-five thousand options. We used evolutionary computation to derive rational strategies and parameters for ORB. The proposed framework based on a genetic algorithm optimizes the parameters related to threshold selection and protective closing strategies. In experiments, this resulted in annual returns of 9.3% (representing a 2.8% improvement over the original strategy) and Sharpe ratio of 2.5 (an improvement of 1.0), while reducing the maximum drawdown by half. The proposed scheme also reduced computational overhead by 89% compared to a grid search. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is one of the heavily studied fields of data mining, which is due to its high compatibility with real-world applications. HUIM is a process of extracting a complete set of interesting patterns by considering the importance and the quantity of each item. Incremental high-utility itemset mining (IHUIM) further increases the compatibility of HUIM by mining from a data stream instead of a static database. It does this by mining interesting patterns in a single database scan and storing prior knowledge in a compact data structure. However, there is a critical limitation, because the importance of each item has to be guaranteed to remain unchanged throughout the data stream. This limitation hinders the compatibility of IHUIM to the real-world applications, because the importance of an item changes from time to time in the real world. Conventional IHUIM approaches consequently have to use outdated information when mining interesting patterns. This paper proposes a novel problem of mining high-utility itemsets in an incremental and dynamic profit environment in order to account for the fluctuation of the item's importance. Furthermore, a novel approach to mining patterns in this type of environment is introduced using an efficient list structure and tight upper bounds. Experiments on real and synthetic datasets show that the proposed approach performs well compared to the state-of-the-art approaches in terms of the runtime and memory usage, and it scales better than the other approaches. [ABSTRACT FROM AUTHOR]

Video salient object detection targets at extracting the most conspicuous objects in a video sequence, which facilitate various video processing tasks, e.g., video compression, video recognition, etc. Although remarkable progress has been made for video salient object detection, most existing methods still suffer from coarse edge boundaries which may hinder their usage in real-world applications. To alleviate this problem, in this paper, we propose a Motion Context guided Edge-preserving network (MCE-Net) model for video salient object detection. MCE-Net can generate temporally consistent salient edges, which are then leveraged to refine the salient object regions completely and uniformly. The core innovation in MCE-Net is an Asymmetric Cross-Reference Module (ACRM), which is designed to exploit the cross-modal complementarity between spatial structure and motion flow, facilitating robust salient object edge extraction. To leverage the extracted edge features for salient object refinement, we fuse them with multi-level spatial–temporal embeddings in a paralleled guidance manner, generating the final saliency results. The proposed method is end-to-end trainable and the edge annotations are generated automatically from ground truth saliency maps. Experimental evaluations on five widely-used benchmarks demonstrate that our proposed method can achieve superior performance to other outstanding methods. Moreover, the experimental results indicate that our method can preserve salient objects with clear boundary structures in video sequences. [Display omitted] • We propose to address the coarse boundary issue in video salient object detection. • A novel method that uses object boundaries to refine salient objects is presented. • The complementarity between spatial and motion cues is exploited to generate edges. • Evaluations on five benchmarks verify the efficacy of the edge refinement strategy. [ABSTRACT FROM AUTHOR]

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database. • We formally define the group trajectory outlier detection problem. • We propose several solutions based on data mining and deep learning models to identify outliers. • We evaluate various algorithms using human-behavioral databases to identify the performance of two different models. [ABSTRACT FROM AUTHOR]

High Utility Pattern Mining is a popular task for analyzing data. It consists of discovering patterns having a high importance in databases. A popular application of high utility pattern mining is to identify high utility (profitable) patterns in customer transaction data. Though such analysis can be useful to understand data, it does not consider the cost (e.g. effort, resources, money or time) required for obtaining the utility (benefits). In this paper, we argue that to discover interesting patterns in event sequences, it is useful to consider both a utility model and a cost model. For example, to identify cost-effective ways of treating patients from medical pathways data, it is desirable to consider not only the ability of treatments to inhibit symptoms or cure a disease (utility) but also the resources consumed and the time spent (cost) to provide these treatments. Based on this perspective, this paper defines a novel task of discovering Cost-Effective Event Sequences in event logs. In this task, cost is modeled as numeric values, while utility is represented either as binary or numeric values. Measures are proposed to evaluate the trade-off and correlation between cost and utility of patterns to identify cost-effective patterns (patterns having a low cost but providing a high utility). Three efficient algorithms called CEPB, corCEPB and CEPN are designed to extract these patterns. They rely on a tight lower-bound on the cost and a memory buffering technique to find patterns efficiently. Experiments show that the proposed algorithms achieve high efficiency, that proposed optimizations improve efficiency, and that insightful cost-effective patterns are found in real-life e-learning data. [ABSTRACT FROM AUTHOR]

Accurate determination of cable force values is the most important technical means to avoid damage to the cable bridge. In order to avoid the influence of the difficulty in distinguishing the boundary conditions and the lack of low-order natural frequency on the cable force determination results, an intelligent method for determining the bridge cable force based on the vibration method is proposed. With the cable length, linear density, flexural stiffness and input frequency as input units and the cable force as output unit, a neural network is established to identify the cable force by combining the finite element simulation data, and the model is optimized using the intelligent swarm optimization algorithm. The results show that compared with the cable force prediction models using generalized regression neural network (GRNN) and GRNN optimized using particle swarm optimization (PSO-GRNN) and canonical identification methods, the GRNN optimized using sparrow search algorithm (SSA-GRNN) proposed in this paper has a better identification effect. The prediction error of short cables is essentially within 10%, and that of long cables is within 5%. It can not only realize the accurate identification of bridge cable force by ignoring the boundary conditions and vibration frequency order of cables, but also has a wide range of applications. • Establish finite element model to provide data basis for the design model. • Identify cable force ignoring the boundary conditions and vibration frequency order. • GRNN is optimized by SSA and PSO to improve the performance of the design model. [ABSTRACT FROM AUTHOR]

High utility pattern mining has been actively researched and applied to diverse applications because it can process the database by considering the quantity and importance of items. However, traditional high utility pattern mining methods aim to handle static databases, so they cannot meet the requirements of users who want to process the dynamic environments. Although methods to process incremental databases have been proposed, they have limitations that they perform the mining process on the entire database, including already processed data, whenever data are newly inserted. The pre-large concept is one of the techniques to process the dynamic database. Utilizing the pre-large technique, we can efficiently handle the transaction insertion using the extracted patterns of the previous mining process. In this paper, we propose a novel pre-large-based approach to discover high utility patterns from incremental databases. A list structure is proposed to store the utility information of patterns, so candidate patterns are not generated, and an additional database scan is not required. Performance evaluation performed on various real and synthetic datasets shows that the proposed algorithm is more efficient and effective than the latest approaches in a dynamic environment. [ABSTRACT FROM AUTHOR]

In the Recirculating Aquaculture Systems (RAS), the control of water quality indices remains essential to survival and growth of aquaculture objects. This requires effect prediction of future water status in advance, which can be adopted to help the generation of following control strategies. However, conventional methods of water quality prediction were mostly dependent on redundant parameters of model, which leads to inefficiency and low accuracy. In addition, the complexity of the RAS multi-units requires intelligent control of the water quality unit. Thus, a prediction and control framework for predicting water quality in RAS is proposed in this paper. Specifically, a hybrid deep learning structure which combines the Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Attention mechanism is presented. To begin with, the CNN is utilized to extract local features for different timestamped water quality parameter. After the local features have been extracted, the proposed GRU model replicates the global sequential features of the parameters. The attention mechanism is then applied to focus on more critical features to promote the efficiency and accuracy of prediction. Finally, to demonstrate the efficiency and stability of the prediction and control framework with the mixture of CNN, GRU and Attention (PC-CGA), multiple groups of experiments and evaluations are carried out in a medium size RAS. [ABSTRACT FROM AUTHOR]

Stability and security issues in energy management have become widespread research topics, in which artificial intelligence techniques are often embedded in management systems to efficiently manage the smart grid. In this paper, we propose an energy grid management system with anomaly detection and Q-learning decision modules (EMSAD). The anomaly detection module is a multitask learning network that simultaneously classifies suppliers and predicts actual supply quantities. The Q-learning decision module then determines the operating reserve and subsidies to manage the energy grid. Experimental results show that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers with F1-scores from 73.3% to 100.0%. The robustness evaluation also shows that EMSAD maintains high performance even in unseen environments without fine-tuning. Thus, the simulation results demonstrate the security, efficiency, transferability, and robustness of the proposed EMSAD in smart grid energy management. [ABSTRACT FROM AUTHOR]

Data mining has been actively studied, and it has become more important due to the development of information technology and the demands of diverse applications, such as the retail market, medical field, and manufacturing plants. High average-utility pattern mining is a type of data mining that finds valuable patterns by dividing the utility of a pattern by the length of the pattern. It considers the length of the pattern, so it can handle the demerits of utility pattern mining and extract more important patterns. Meanwhile, stream data is produced in real time, and the importance of the recent data gradually increases as opposed to the old data. However, most of the previous high average utility pattern mining approaches cannot find the latest valuable patterns over the data streams. Although research using a sliding window model was conducted to handle only the recent data, it has a problem due to the data structure. Since the previous work is a tree-based approach, numerous candidate patterns are generated, and an additional database scan is required to calculate the actual utilities of candidates. Creating the candidate patterns reduces the efficiency of runtime and memory and makes real-time data processing difficult. We propose an efficient algorithm that uses a list structure for sliding window-based high average-utility pattern mining in this paper. The proposed algorithm scans the stream data once, mines the complete set of the recent high average-utility patterns without generating candidates, and reduces the search space by using a novel pruning technique. The experiment results show that the proposed algorithm outperforms the state-of-the-art algorithms in terms of the runtime, memory, and scalability in real and synthetic datasets. [ABSTRACT FROM AUTHOR]

Recently, numerous Machine Learning (ML) algorithms have been applied in many areas of cybersecurity. However, most of these systems can only be seen as a black box to users. To improve our understanding of such systems, adversarial machine learning approaches can be used. The main features are detected by analyzing the extent of such changes, which helps in identifying the main reasons for misclassification. In this paper, the presented approach has obtained satisfactory results that accurately explains the reasons for misclassifications. Some features of the presented method can be applied to any classifier with defined gradients without the need for modifications. The proposed model can be extended to perform more diagnoses and it can be used for a deeper analysis of systems, obtaining more than 95% accuracy classification on the used datasets in the experiments. [Display omitted] • Explains misclassifications by data-driven AI models using an adversarial approach. • Compute the minimum number of changes to input features required. • Increased average classification accuracy by 2.5% post modification. • Designed a black-box attack to test the correctness and trustworthiness. • Used explanation maps to examine the effectiveness of attacks. [ABSTRACT FROM AUTHOR]

Cloud Computing services can be accessed anytime, anywhere via the Internet. The overwhelming growth of cloud data centers over the past decade has increased their costs as energy demands have risen. As a result, higher carbon dioxide emissions and other greenhouse gasses are putting a strain on our ecosystem. The main objective of this study is to reduce the power consumption in cloud computing with no or negligible trade-offs in quality of service. This paper presents a new algorithm called the energy efficiency heuristic using virtual machine consolidation to minimize the high energy consumption in the cloud. By setting two thresholds, hosts are classified into three main classes. The designed model reallocates virtual machines from one physical host to another to minimize energy consumption. The results of the proposed algorithm have been obtained in terms of virtual machine migrations, performance degradation caused by migration, service level agreement violations, and execution time, showing a significant improvement over state-of-the-art techniques. • A novel scheduling mechanism EEHVMC to reduce energy consumption is designed. • The designed EEHVMC can also reduce VR migration, and service level agreement violations. • The designed model is based on the real-time cloud computing. [ABSTRACT FROM AUTHOR]

High utility pattern mining (HUPM) discovers meaningful patterns by considering features of items and utility from non-binary data. Data called stream data is continually generated over time. Various techniques based on high utility pattern mining have been suggested for processing stream data. High utility pattern mining based on a sliding window performs pattern mining using a window. Since it uses only the data stored in a window, only the latest data can be managed. However, Stream data has the property that newly created data has a higher influence than relatively old data. It is necessary to consider the importance of the data stored in a window differently. In this paper, we propose an efficient algorithm based on a sliding window approach that mines high utility patterns considering the latest data more significantly from damped stream data where new data is constantly being inserted. In other words, our technique divides the stream data into fixed-sized multiple batch data and processes differently the importance of each batch data in a window according to the added time using the decaying factor. Moreover, we conduct experiments to compare and analyze our approach with the state-of-the-art algorithms using real and synthetic datasets. The experimental results show that our proposed method outperforms the competitors in terms of run time, memory usage, and scalability test. [ABSTRACT FROM AUTHOR]