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There are many real-life data incrementally generated around the world. One of the recent interesting issues is the efficient processing real-world data that is continuously accumulated. Mining and recognizing removable patterns in such data is a challenging task. Erasable pattern mining confronts this challenge by discovering removable patterns with low gain. In various real-world applications, data are stored in the form of non-binary databases. These databases store item information in a quantity form. Since items in the database can each have different characteristics, such as quantities, considering their relative features makes the mined patterns more meaningful. For these reasons, we propose an erasable utility pattern mining algorithm for incremental non-binary databases. The suggested technique can recognize removable patterns by considering the relative utility of items and the profit of products in an incremental database. The proposed algorithm utilizes a list structure for efficiently extracting erasable utility patterns. Several experiments have been conducted to compare the performance between the suggested algorithm and state-of-the-art techniques using real and synthetic datasets, and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Sensor data that is often collected in the Internet of Things (IoT) or any smart city environment should be protected against security and privacy concerns. Often, sensor data that is shared by devices in smart cities contains sensitive or private information that can often be shared over different networks and by different smart applications. In the last decade, the area of Privacy-Preserving Data Mining (PPDM) has received a lot of attention as the amount of data received and collected daily is huge. Unfortunately, PPDM mostly applies to binary data. To improve the usefulness of PPDM, we present a more usable version for smart cities called Privacy-Preserving Utility Mining (PPUM), in the form of a Maximal Sensitive Utility-Maximal Sensitive ConflIct (MSU-MSI) algorithm. MSU-MSI finds any conflicting items that may contain sensitive itemsets with high-utility and sanitizes them, stripping them of sensitive and private information while maintaining utility. Any transactions encountered that contain sensitive itemsets are first fed through sanitization processes. This is followed by calculating the total number of items that conflict, and then removing them so sanitization processes can operate more efficiently so as to not redo known sanitization processes. We conduct an in-depth experimental analysis, where our detailed methodology is compared directly with state-of-the-art frameworks such as MSU-MIU, MSU-MAU, HHUIF and MSCIF. Our proposed MSU-MSI shows a higher performance in missing cost, in particular when dealing with highly dense or highly sparse datasets. Moreover, our novel framework is shown to achieve an excellent performance with regards to similarity in database structure and database utility. [ABSTRACT FROM AUTHOR]

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes’ neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased F-measure by 5.06% and 2.4% while compared with generic Node2Vec and Struct2Vec methods. [ABSTRACT FROM AUTHOR]

Many research areas depend on group anomaly detection. The use of group anomaly detection can maintain and provide security and privacy to the data involved. This research attempts to solve the deficiency of the existing literature in outlier detection thus a novel hybrid framework to identify group anomaly detection from sequence data is proposed in this paper. It proposes two approaches for efficiently solving this problem: i) Hybrid Data Mining-based algorithm, consists of three main phases: first, the clustering algorithm is applied to derive the micro-clusters. Second, the $kNN$ algorithm is applied to each micro-cluster to calculate the candidates of the group’s outliers. Third, a pattern mining framework gets applied to the candidates of the group’s outliers as a pruning strategy, to generate the groups of outliers, and ii) a GPU-based approach is presented, which benefits from the massively GPU computing to boost the runtime of the hybrid data mining-based algorithm. Extensive experiments were conducted to show the advantages of different sequence databases of our proposed model. Results clearly show the efficiency of a GPU direction when directly compared to a sequential approach by reaching a speedup of 451. In addition, both approaches outperform the baseline methods for group detection. [ABSTRACT FROM AUTHOR]

This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching‐based Instances (COMI) and Pattern mining for Ontology Matching‐based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart‐city data into similar clusters using the generic k‐means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state‐of‐the‐art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal with heterogeneous large‐scale data in smart‐city environments. [ABSTRACT FROM AUTHOR]

Recently, revealing more valuable information except for quantity value for a database is an essential research field. High utility itemset mining (HAUIM) was suggested to reveal useful patterns by average-utility measure for pattern analytics and evaluations. HAUIM provides a more fair assessment than generic high utility itemset mining and ignores the influence of the length of itemsets. There are several high-performance HAUIM algorithms proposed to gain knowledge from a disorganized database. However, most existing works do not concern the uncertainty factor, which is one of the characteristics of data gathered from IoT equipment. In this work, an efficient algorithm for HAUIM to handle the uncertainty databases in IoTs is presented. Two upper-bound values are estimated to early diminish the search space for discovering meaningful patterns that greatly solve the limitations of pattern mining in IoTs. Experimental results showed several evaluations of the proposed approach compared to the existing algorithms, and the results are acceptable to state that the designed approach efficiently reveals high average utility itemsets from an uncertain situation. [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]

Periodic pattern mining models analyze patterns which occur periodically in a time-series database, such as sensor readings of smartphones and/or Internet of Things devices. The extracted patterns can be utilized for risk prediction, system management, and decision-making. In this article, we propose an efficient periodicity-oriented data analytics approach. It ignores intermediate events deliberately by adopting the concept of flexible periodic patterns, so it can be applied to more diverse real-life scenarios and systems. Moreover, the proposed approach adopts a novel symbol-centered data structure instead of existing data structures for state-of-the-art approaches of periodic pattern mining. Performance evaluations on real-life datasets, Diabetes, Oil Prices, and Bike Sharing, and requirements show that our approach has better runtime, memory usage, number of visited patterns, and sensitivity than efficient periodic pattern mining (EPPM) and flexible periodic pattern mining (FPPM), which are the state-of-the-art approaches in the same field. The experimental results show that the proposed algorithm will require less runtime and smaller memory than the existing algorithms on most data and requirements in real life. [ABSTRACT FROM AUTHOR]

In industrial areas, most of databases are dynamic databases, and the volume of the databases has grown with the passage of time. Especially, pattern mining for incremental database needs different approaches from static database because the profit or the accuracy of the previously inserted data can be reduced. Since data is time‐ sensitive, the recent data has a relatively higher value than the old data. In this paper, we suggest the damped window based average utility driven data analytics for intelligent systems, which the damped window reflects the importance according to the arrival time of the transactions. The proposed mining approach adopts novel data structure, which modify the importance of item as the passage of time, and it improves mining efficiency with several pruning strategies and without generating candidate patterns. To evaluate the performance of the proposed mining approach, we conducted various experiments using several real and synthetic data sets. The result of the experiments presented that the suggested method performs better in terms of runtime and memory usage than the other state‐of‐the‐art mining techniques. Moreover, through the scalability experiments, which changed the number of different items or transactions, we verified that the proposed algorithm maintained a stable performance under various environmental changes. [ABSTRACT FROM AUTHOR]

This paper addresses the taxi fraud problem and introduces a new solution to identify trajectory outliers. The approach as presented allows to identify both individual and group outliers and is based on a two phase-based algorithm. The first phase determines the individual trajectory outliers by computing the distance of each point in each trajectory, whereas the second identifies the group trajectory outliers by exploring the individual trajectory outliers using both feature selection and sliding windows strategies. A parallel version of the algorithm is also proposed using a sliding window-based GPU approach to boost the runtime performance. Extensive experiments have been carried out to thoroughly demonstrate the usefulness of our methodology on both synthetic and real trajectory databases. The results show that the GPU approach enables reaching a speed-up of 341 over the sequential algorithm on large synthetic databases. The efficiency of the proposed method to detect both individual and group trajectory outliers on a real-world taxi trajectory database is also demonstrated in comparison with baseline trajectory outlier and group detection algorithms. The results are very promising and show superiority of the proposed method both in reducing computational time and enhancing the quality of returned outliers. Finally, we prime our methodology and results for future refinement using deep learning methodologies. [ABSTRACT FROM AUTHOR]

Databases that deal with the real world have various characteristics. New data is continuously inserted over time without limiting the length of the database, and a variety of information about the items constituting the database is contained. Recently generated data has a greater influence than the previously generated data. These are called the time-sensitive non-binary stream databases, and they include databases such as web-server click data, market sales data, data from sensor networks, and network traffic measurement. Many high utility pattern mining and stream pattern mining methods have been proposed so far. However, they have a limitation that they are not suitable to analyze these databases, because they find valid patterns by analyzing a database with only some of the features described above. Therefore, knowledge-based software about how to find meaningful information efficiently by analyzing databases with these characteristics is required. In this article, we propose an intelligent information system that calculates the influence of the insertion time of each batch in a large-scale stream database by applying the sliding window model and mines recent high utility patterns without generating candidate patterns. In addition, a novel list-based data structure is suggested for a fast and efficient management of the time-sensitive stream databases. Moreover, our technique is compared with state-of-the-art algorithms through various experiments using real datasets and synthetic datasets. The experimental results show that our approach outperforms the previously proposed methods in terms of runtime, memory usage, and scalability. [ABSTRACT FROM AUTHOR]

When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors critically, which gives HUIM its intrinsic edge. Due to the flourishing development of the IoT technique, the uncertainty patterns mining is also attractive. Potential high-utility itemset mining (PHUIM) is introduced to reveal valuable patterns in an uncertainty database. Unfortunately, even though the previous methods are all very effective and powerful to mine, the potential high-utility itemsets quickly. These algorithms are not specifically designed for a database with an enormous number of records. In the previous methods, uncertainty transaction datasets would be load in the memory ultimately. Usually, several pre-defined operators would be applied to modify the original dataset to reduce the seeking time for scanning the data. However, it is impracticable to apply the same way in a big-data dataset. In this work, a dataset is assumed to be too big to be loaded directly into memory and be duplicated or modified; then, a MapReduce framework is proposed that can be used to handle these types of situations. One of our main objectives is to attempt to reduce the frequency of dataset scans while still maximizing the parallelization of all processes. Through in-depth experimental results, the proposed Hadoop algorithm is shown to perform strongly to mine all of the potential high-utility itemsets in a big-data dataset and shows excellent performance in a Hadoop computing cluster. [ABSTRACT FROM AUTHOR]

Modern Internet of Things (IoT) applications generate massive amounts of data, much of them in the form of objects/items of readings, events, and log entries. Specifically, most of the objects in these IoT data contain rich embedded information (e.g., frequency and uncertainty) and different levels of importance (e.g., unit risk/utility of items, interestingness, cost, or weight). Many existing approaches in data mining and analytics have limitations, such as only the binary attribute is considered within a transaction, as well as all the objects/items having equal weights or importance. To solve these drawbacks, a novel utility-driven data analytics algorithm named HUPNU is presented in this article. As a general utility-driven uncertain data mining model, HUPNU can extract High-Utility patterns by considering both Positive and Negative unit utilities from Uncertain data. The qualified high-utility patterns can be effectively discovered for intrusion detection, risk prediction, manufacturing management, and decision-making, among others. By using the developed vertical Probability-Utility list with the positive and negative utilities structure, as well as several effective pruning strategies, experiments showed that the developed HUPNU approach with the pruning strategies performed great in mining the qualified patterns efficiently and effectively. [ABSTRACT FROM AUTHOR]

The emergence and proliferation of the internet of things (IoT) devices have resulted in the generation of big and uncertain data due to the varied accuracy and decay of sensors and their different sensitivity ranges. Since data uncertainty plays an important role in IoT data, mining the useful information from uncertain dataset has become an important issue in recent decades. Past works focus on mining the high sequential patterns from the uncertain database. However, the utility of a derived sequence increases along with the size of the sequence, which is an unfair measure to evaluate the utility of a sequence since any combination of a high-utility sequence will also be the high-utility sequence, even though the utility of a sequence is merely low. In this paper, we address the limitation of the previous potential high-utility sequential pattern mining and present a potentially high average-utility sequential pattern mining framework for discovering the set of potentially high average-utility sequential patterns (PHAUSPs) from the uncertain dataset by considering the size of a sequence, which can provide a fair measure of the patterns than the previous works. First, a baseline potentially high average-utility sequential pattern algorithm and three pruning strategies are introduced to completely mine the set of the desired PHAUSPs. To reduce the computational cost and accelerate the mining process, a projection algorithm called PHAUP is then designed, which leads to a reduction in the size of candidates of the desired patterns. Several experiments in terms of runtime, number of candidates, memory overhead, number of discovered pattern, and scalability are then evaluated on both real-life and artificial datasets, and the results showed that the proposed algorithm achieves promising performance, especially the PHAUP approach. [ABSTRACT FROM AUTHOR]

Most pattern mining approaches such as association rule mining or frequent itemsets mining can only handle, however, the binary database in which each item or attribute is represented as 0 or 1. In real-life situations, data may be represented as more complicatedly. Moreover, it is more meaningful for human begins to understand if the discovered rules can be represented as the linguistic terms. Fuzzy-set theory was adopted to handle the quantitative database, which transforms the quantity value into the linguistic terms with their fuzzy values based on the pre-defined membership functions. Several algorithms were designed to handle this issue, but most of them focus on mining the fuzzy rules in the static database. When the database size is changed, the whole updated database should be rescanned again to obtain the up-to-date information. This progress ignores the already discovered knowledge with its computational cost, which is inefficient in dynamic applications. In this paper, we present an incremental multiple fuzzy frequent itemset mining with transaction INSertion (IMF-INS) algorithm to efficiently update the multiple fuzzy frequent itemsets from the quantitative dataset. The designed IMF-INS utilizes the fuzzy FUP concept to divide the transformed linguistic terms into four cases. Each case is performed by the designed approach to update the discovered information. Also, the fuzzy list structure is utilized to reduce the generation of candidates without multiple database scans. Experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, number of determined patterns and scalability. Results indicated that the designed IMF-INS outperforms the state-of-the-art batch mining progresses. [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]

High-utility itemset mining (HUIM) is an extension of traditional frequent itemset mining, which considers both quantities and unit profits of items in a database to reveal highly profitable itemsets regardless of their size. High average-utility itemset mining (HAUIM) is designed to find average-utility itemsets by considering both their utility and the number of items that they contain. Thus, average-utility itemsets are obtained based on a fair utility measurement since the average utility typically does not increase much with the size of itemsets. However, most algorithms for discovering high average utility itemsets are designed to extract patterns from a static database. If the size of a database decreases or increases over time (e.g., as a result of transaction insertions), the database must be scanned again in batch mode to update the results. Thus, previously discovered knowledge is ignored and the time previously spent for pattern extraction is wasted. We thus present an incremental HAUIM algorithm for transaction insertion (FUP-HAUIMI) to maintain information about patterns when a database is updated, based on the FUP concept. An average-utility-list (AUL)-structure is first built by scanning the original database. Then, FUP-HAUIMI selects high average-utility upper-bound itemsets and categorizes them according to four cases. For each case, itemsets are maintained and updated using a specific updating procedure. While traversing the enumeration tree representing the search space in a depth-first way, a join operation is performed to quickly and incrementally update the AUL-structures. Several experiments were carried to evaluate the runtime, memory usage, number of potential patterns (candidates), and the scalability of the designed approach. Results show that the performance of FUP-HAUIMI is excellent compared to the state-of-the-art HAUI-Miner algorithm running in batch mode and the state-of-the-art incremental high-utility pattern mining (IHAUPM) algorithm for incremental average-utility pattern mining. [ABSTRACT FROM AUTHOR]

Traditional association rule mining has been widely studied. But it is unsuitable for real‐world applications where factors such as unit profits of items and purchase quantities must be considered. High‐utility itemset mining (HUIM) is designed to find highly profitable patterns by considering both the purchase quantities and unit profits of items. However, most HUIM algorithms are designed to be applied to static databases. But in real‐world applications such as market basket analysis and business decision‐making, databases are often dynamically updated by inserting new data such as customer transactions. Several researchers have proposed algorithms to discover high‐utility itemsets (HUIs) in dynamically updated databases. Unlike batch algorithms, which always process a database from scratch, incremental high‐utility itemset mining (iHUIM) algorithms incrementally update and output HUIs, thus reducing the cost of discovering HUIs. This paper provides an up‐to‐date survey of the state‐of‐the‐art iHUIM algorithms, including Apriori‐based, tree‐based, and utility‐list‐based approaches. To the best of our knowledge, this is the first survey on the mining task of incremental high‐utility itemset mining. The paper also identifies several important issues and research challenges for iHUIM. WIREs Data Mining Knowl Discov 2018, 8:e1242. doi: 10.1002/widm.1242 This article is categorized under: Algorithmic Development > Association Rules Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Knowledge Representation [ABSTRACT FROM AUTHOR]

Mining frequent and high utility itemsets (FHUIs) from transactional databases is essential in data mining. From a multiobjective perspective, modelling the task of mining FHUIs in a unified framework requires support and utility to be considered simultaneously. In contrast to traditional algorithms for mining FHUIs, multiobjective evolutionary algorithms (MOEAs) can overcome the difficulty of setting the parameter and can generate multiple solutions in one pass, which brings advantages to mining FHUIs. However, MOEAs may be inefficient when the number of transactions and the number of items in the transaction database are large. To address this problem, we propose an efficient biobjective evolutionary algorithm for obtaining FHUIs (BOEA-FHUI) based on three novel strategies. In BOEA-FHUI, a pruning strategy is proposed to reduce the search space. Based on the pruning results, a repair strategy is proposed to make the generated inferior offspring jump out of the dominated region of the previous Pareto solutions. With the proposed pruning and repair strategies, the search space can be significantly reduced, which helps improve the search efficiency. To increase the number of items with higher support and higher utility values, an improved mutation strategy based on the sparse nature of the FHUI is proposed, which can accelerate the convergence speed of the algorithm. The experimental results on the real-world and synthetic datasets show that the proposed algorithm performs better than state-of-the-art MOEAs in finding FHUIs. • An efficient bi-objective evolutionary algorithm for mining FHUIs is proposed. • A pruning strategy is introduced based on support measure and TWU measure. • A repair strategy is proposed to make the offspring jump out of the dominated area. • An novel mutation strategy is designed based on the sparse nature of FHUI. [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]

Mining sequential patterns (SPs) is a popular data mining task, which consists in finding interesting, unexpected, and useful patterns in sequence databases. It has several applications in many domains. However, most sequential pattern mining algorithms assume that databases are static, i.e. that they do not change over time. But in real-word applications, sequences are often modified. Thus, it is an important issue to design algorithms for updating SPs in a dynamic database environment. Although some algorithms have been proposed to maintain SPs in dynamic databases, these algorithms may have poor performance, especially when databases contain long sequences or a large number of sequences. This paper addresses this issue by proposing a novel dynamic mining approach named PreFUSP-TREE-MOD to address the problem of maintaining and updating discovered SPs when sequences in a database are modified. The proposed approach adopts the previously proposed pre-large concept using two support thresholds, to avoid scanning the database when possible, for updating the set of discovered patterns. Due to the pruning properties of the pre-large concept, the PreFUSP-TREE-MOD maintenance algorithm can effectively reduce the cost of database scans to maintain and update the built FUSP-tree for sequence modification. When the number of modified sequences is less than the safety bound of the pre-large concept, the proposed maintenance algorithm outperforms traditional SPM algorithms in batch mode, and the state-of-the-art maintenance algorithm in terms of execution time and number of tree nodes. [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]

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]

Frequent itemsets are useful for discovering interesting associations hidden in large databases. Many mining algorithms use data with binary attributes to represent the occurrence of items and find frequent itemsets. However, many real-world applications provide a richer source of transactions with quantitative values. The fuzzy frequent pattern tree algorithm was thus proposed for extracting fuzzy frequent itemsets from the quantitative transactions. In this paper, a tree structure called the upper-bound multiple fuzzy frequent-pattern (UBMFFP)-tree is designed for improving the pruning effect in the mining process. A two-phase fuzzy mining approach based on the tree structure is also proposed to obtain the complete fuzzy frequent itemsets from a quantitative database. The proposed fuzzy mining approach recursively and efficiently finds the upper-bound fuzzy counts of itemsets with the aid of the tree structure. It prunes unpromising itemsets in the first phase, and then finds the actual fuzzy frequent itemsets in the second phase. Experimental results indicate that the proposed UBMFFP-tree algorithm has good performance in terms of execution time and number of tree nodes. [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]

Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns. [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]