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It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which identifies itemsets with high utility from the transaction database. This gains popularity in data mining, because it identifies itemsets which have more value but the same was not identified as frequent by Frequent Itemset Mining. HUIM is generally referred to as Utility Mining. The utility of the items is measured based on parameters like cost, profit, quantity or any other measures preferred by the users. Compared to high utility itemsets (HUIs) mining, high average utility itemsets (HAUIs) mining is more precise by considering the number of items in the itemsets. In state-of-the-art algorithms that mines HUIS and HAUIs use a single fixed minimum utility threshold based on which HAUIs are identified. In this paper, the proposed algorithm mines HAUIs from transaction databases using Artificial Fish Swarm Algorithm (AFSA) with computed multiple minimum average utility thresholds. Computing the minimum average utility threshold for each item with the AFSA algorithm outperforms other state-of-the-art HAUI mining algorithms with multiple minimum utility thresholds and user-defined single minimum threshold in terms of number of HAUIs. It is observed that the proposed algorithm outperforms well in terms of execution time, number of candidates generated and memory consumption when compared to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Data clustering is a machine learning method for unsupervised learning that is popular in the two areas of data analysis and data mining. The objective is to partition a given dataset into distinct clusters, aiming to maximize the similarity among data objects within the same cluster. In this paper, an improved honey badger algorithm called DELHBA is proposed to solve the clustering problem. In DELHBA, to boost the population's diversity and the performance of global search, the differential evolution method is incorporated into algorithm's initial step. Secondly, the equilibrium pooling technique is included to assist the standard honey badger algorithm (HBA) break free of the local optimum. Finally, the updated honey badger population individuals are updated with Levy flight strategy to produce more potential solutions. Ten famous benchmark test datasets are utilized to evaluate the efficiency of the DELHBA algorithm and to contrast it with twelve of the current most used swarm intelligence algorithms and k-means. Additionally, DELHBA algorithm's performance is assessed using the Wilcoxon rank sum test and Friedman's test. The experimental results show that DELHBA has better clustering accuracy, convergence speed and stability compared with other algorithms, demonstrating its superiority in solving clustering problems. [ABSTRACT FROM AUTHOR]

The researcher proposed the concept of Top-K high-utility itemsets mining over data streams. Users directly specify the number K of high-utility itemsets they wish to obtain for mining with no need to set a minimum utility threshold. There exist some problems in current Top-K high-utility itemsets mining algorithms over data streams including the complex construction process of the storage structure, the inefficiency of threshold raising strategies and utility pruning strategies, and large scale of the search space, etc., which still can not meet the requirement of real-time processing over data streams with limited time and memory constraints. To solve this problem, this paper proposes an efficient algorithm based on dataset projection for mining Top-K high-utility itemsets from a data stream. A data structure CIUDataListSW is also proposed, which stores the position of the item in the transaction to effectively obtain the initial projected dataset of the item. In order to improve the projection efficiency, this paper innovates a new reorganization technology for projected transactions in common batches to maintain the sort order of transactions in the process of dataset projection. Dual pruning strategy and transaction merging mechanism are also used to further reduce search space and dataset scanning costs. In addition, based on the proposed CUDHSW structure, an efficient threshold raising strategy CUD is used, and a new threshold raising strategy CUDCB is designed to further shorten the mining time. Experimental results show that the algorithm has great advantages in running time and memory consumption, and it is especially suitable for the mining of high-utility itemsets of dense datasets. [ABSTRACT FROM AUTHOR]

The association rule algorithm in data mining is used to study the factors that may affect students' performance, to make suggestions for teaching work, and to provide decision-making basis for teachers and teaching administrators, which has practical significance. There are many potential applications for facial expression recognition technology. For example, in the teaching process, facial expression recognition technology helps teachers understand students and judge students' reactions to certain things. Based on the current research status of emotion recognition and data mining algorithms, this paper improves the AprioriTid algorithm and constructs an online teaching quality evaluation model based on teaching needs. In addition, this article applies the model constructed in this article to the evaluation of English online teaching quality and evaluates teaching quality through data mining. The experimental research shows that the model constructed in this paper has good performance. [ABSTRACT FROM AUTHOR]

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective. [ABSTRACT FROM AUTHOR]

Outlier detection is an important branch of data mining. This paper proposes an advanced fast density peak outlier detection algorithm based on the characteristics of big data. The algorithm is an outlier detection method based on the improved density peak clustering algorithm. This paper improves the original algorithm. From the perspective of outlier detection, although it is a clustering idea, it avoids the clustering process, reduces the time complexity of the cluster-based outlier detection algorithm, and absorbs. The outlier detection based on neighbors is not sensitive to data dimensions and other advantages. In the power industry, outlier detection can be used in areas such as grid fault detection, equipment fault detection, and power abnormality detection. The simulation experiment of outlier detection based on the daily load curve of single and multiple transformers in a certain province shows that the improved algorithm can effectively detect outliers in the data. [ABSTRACT FROM AUTHOR]

The economic interaction between the countries of the world is gradually strengthening. Among them, the US stock market is a "barometer" of the global economy, which has a huge impact on the global economy. Therefore, it is of great significance to study the data in the US stock market, especially the data mining algorithm of abnormal data. At present, although data mining technology has achieved many research results in the financial field, it has not formed a good research system for time series data in stock market anomalies. According to the actual performance and data characteristics of the stock market anomaly, this paper uses data mining techniques to find the abnormal data in the stock market data, and uses the isolated point detection method based on density and distance to analyze the obtained abnormal data to obtain its implicit useful information. However, due to the defects of traditional data mining algorithms in dealing with stock market anomalies containing uncertain factors, that is, the errors caused by other human factors, this paper introduces the roughening entropy of the uncertainty data and applies its theory to the field of data mining, a data mining algorithm based on rough entropy in the US stock market anomaly is designed. Finally, the empirical analysis of the algorithm is carried out. The experimental results show that the data mining algorithm based on rough entropy proposed in this paper can effectively detect the abnormal fluctuation of time series in the stock market. [ABSTRACT FROM AUTHOR]

Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time. [ABSTRACT FROM AUTHOR]

It is a challenge for existing artificial intelligence algorithms to deal with incomplete information of computer tactical wargames in military research, and one effective method is to take advantage of game replays based on data mining or supervised learning. However, the open source datasets of wargame replays are extremely rare, which obstruct the development of research on computer wargames. In this paper, a data set of wargame replays is opened for predicting algorithm on the condition of incomplete information, to be specific, we propose the dataset processing method for deep learning and an network model for enemy locations predicting. We first introduce the criteria and methods of data preprocessing, parsing and feature extraction, then the training set and test set for deep learning are predefined. Furthermore, we have designed a newly specific network model for enemy locations predicting, including multi-head input, multi-head output, CNN and GRU layers to deal with the multi-agent and long-term memory problems. The experimental results demonstrate that our method achieves good performance of 84.9% on top-50 accuracy. Finally, we open source the data set and methods on https://github.com/daman043/AAGWS-Wargame-master. [ABSTRACT FROM AUTHOR]

The paper reviews classification algorithms based on Gene Expression Programming (GEP) used for mining the real-life datasets. Our aim is to show, chronologically, most important developments as well as the current state-of-the-art in the area of GEP-based classifiers, with a view to attract further real life applications. We begin with reviewing approaches to building basic, stand alone, GEP classifiers and eventually, combining them into the classifier ensemble. In the following part of the paper we describe and illustrate with example several hybrid solutions where GEP is integrated with other methods. Next, we review specialized and dedicated methods including multiple criteria and incremental GEP-based classification tools. Final part of the paper reviews specialized GEP-based classifiers used to mine the real-life datasets. [ABSTRACT FROM AUTHOR]

In this paper, an in-depth analysis of automated production line faults based on fuzzy algorithms is carried out and based on an in-depth investigation of the mechanism of equipment faults, research work on equipment state prediction and production line fault diagnosis is carried out, and the corresponding algorithm model workflow is given, which has some practical application value for improving the accuracy of production line fault prediction. The algorithm with data mining association rules is proposed to extract the confidence parameters of the conditional state fuzzy net model, and an inverse conditional state fuzzy net is established based on the conditional state fuzzy net for fault diagnosis and reasoning, and a dynamic confidence level reasoning mechanism is also established for reverse reasoning based on the iterative algorithm of maximum algebra. To monitor the operating status of the production line more intuitively, a production line fault prediction and analysis system is developed based on the platform, which mainly includes a data management module, state monitoring module, state prediction module, fault diagnosis module, and maintenance advice module, which can more easily realize the monitoring of the production line equipment state and fault early warning prompting, making the system more practical value. [ABSTRACT FROM AUTHOR]

In this paper, a novel Feature-Reduction Fuzzy C-means (FRFCM) with Feature Linkage Weight (FRFCM-FLW) algorithm is introduced. By the combination of FRFCM and feature linkage weight, a new feature selection model is developed, called a Feature Linkage Weight Based FRFCM using fuzzy clustering. The larger amounts of features are superior to the complication of the problem, and the larger the time that is exhausted in creating the outcome of the classifier or the model. Feature selection has been established as a high-quality method for preferring features that best describes the data under certain criteria or measure. The proposed method presents three stages namely, 1) Data Formation: The process of data collection and data cleaning; 2) FRFCM-FLW. The proposed method can decrease feature elements routinely, and also construct excellent clustering results. The proposed method calculates a novel weight for every feature by combining modified Mahalanobis distance with feature δm variance in FRFCM algorithm; 3) Fuzzy C-means (FCM) cluster. The proposed FRFCM-FLW method proves high Accuracy Rate (AR), Rand Index (RI) and Jaccard Index (JI) ratio when compared to other feature reduction algorithms like WFCM, EWKM, WKM, FCM and FRFCM algorithms. [ABSTRACT FROM AUTHOR]

Facing COVID-19 epidemic, many countries have recently strengthened epidemic prevention and control measures. The reliability of safety management is of great significance to personnel management and control during the COVID-19 epidemic period. The focus of security management of early warning is to monitor and identify the moving target. The current optical flow method is vulnerable to the influence of light changes and background movement, and it is not very accurate for moving target detection in dynamic complex background. In this paper, aiming at the traditional Lucas Kanade optical flow method, the inter frame difference method, mean shift clustering algorithm and morphological processing are combined to optimize and improve on the original basis, so that the moving target detection effect in both simple and complex environments is significantly improved. At the same time, the improved algorithm also reduces the execution time to a certain extent, and has a certain resistance to noise interference such as light changes. This has a certain ability test value for personnel control during the epidemic. [ABSTRACT FROM AUTHOR]

Mining maximal frequent patterns is significant in many fields, but the mining efficiency is often low. The bottleneck lies in too many candidate subgraphs and extensive subgraph isomorphism tests. In this paper we propose an efficient mining algorithm. There are two key ideas behind the proposed methods. The first is to divide each edge of every certain graph (converted from equivalent uncertain graph) and build search tree, avoiding too many candidate subgraphs. The second is to search the tree built in the first step in order, avoiding extensive subgraph isomorphism tests. The evaluation of our approach demonstrates the significant cost savings with respect to the state-of-the-art approach not only on the real-world datasets as well as on synthetic uncertain graph databases. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of satellite technology, remote sensing inversion has been used as an important part of environmental monitoring. Remote sensing inversion has been prepared for large-scale water environment monitoring in the watershed that is difficult for the traditional water environment monitoring methods. This paper will discuss some shortcomings of traditional remote sensing inversion methods, and proposes a remote sensing inversion method based on convolutional neural network, which realizes large-scale remote sensing smart and automatic inversion monitoring of the water environment. The results show that the method is practical and effective, and can achieve high recognition accuracy for water blooms. [ABSTRACT FROM AUTHOR]

With the growth of data volume in transportation system, requirements of big data technologies are rapidly increasing. This paper presented an improved ant colony algorithm by using data analysis technologies of cloud computing and data mining. And the influence of different spatio-temporal feature fusion methods on the steering wheel angle value of intelligent vehicles is explored by feature fusion method. Furthermore, time-constrained and space-constrained networks are utilized to extract the key features that affect the steering wheel angle value. Experimental results show that the proposed algorithm improves the efficiency of data processing and information search by 35%, comparing to traditional ant colony algorithm. It is very effective in the shortest path analysis of ITS. Our research shows that the application of real-time information in the logistics distribution system can make the planning process more dynamic and the prediction results closer to reality. [ABSTRACT FROM AUTHOR]

During the last decade, ensemble clustering has been the subject of many researches in data mining. In ensemble clustering, several basic partitions are first generated and then a function is used for the clustering aggregation in order to create a final partition that is similar to all of the basic partitions as much as possible. Ensemble clustering has been proposed to enhance efficiency, strength, reliability, and stability of the clustering. A common slogan concerning the ensemble clustering techniques is that "the model combining several poorer models is better than a stronger model". Here at this paper, an ensemble clustering method is proposed using the basic k-means clustering method as its base clustering algorithm. Also, this study could raise the diversity of consensus by adopting some measures. Although our clustering ensemble approach has the strengths of kmeans, such as its efficacy and low complexity, it lacks the drawbacks which the kmeans suffers from; such as its problem in detection of clusters that are not uniformly distributed or in the circular shape. In the empirical studies, we test the proposed ensemble clustering algorithm as well as the other up-to-date cluster ensembles on different data-sets. Based on the experimental results, our cluster ensemble method is stronger than the recent competitor cluster ensemble algorithms and is the most up-to-date clustering method available. [ABSTRACT FROM AUTHOR]

At this stage, with the continuous development of data acquisition technology, the mining of high-dimensional spatial data has become a hot issue. And so on. The spatial data mining component studied in this paper is part of the spatiotemporal data middleware. The main research result is to propose a density-based and grid-based extended adjacent spatial clustering method and apply this method to the space for agricultural informatition. In data mining, we also used the middleware technology to complete the agricultural geographic information system based on MapXtreme, and applied the spatial data mining for agricultural informatization to develop the middleware of agricultural variety division, which solved the agricultural informatization. The practical application is of our country. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the era of big data has come. This paper focuses on the analysis and processing of data. How to mine valuable information in large data has been widely concerned by mining frequent itemsets in the data to derive. Association rules are an important part of data mining technology. The analysis of complex network structure based on improved botnet algorithm is the basis of network science. The study of complex network structure is helpful to understand and predict large data of complex network function and behavior. The conclusion of the system in the botnet algorithm process analysis provides some directional suggestions for the improvement of search engine functions and services. In theory, a set of simple and easy botnet algorithm for analyzing the satisfaction factors of clustering is proposed. The practice proves that the big data intelligent search engine based on differential evolution botnet algorithm has significant improvement in retrieval time, recall rate and precision, and can effectively realize personalized and accurate search. A complete big data mining system is implemented, which provides an efficient and easy-to-use tool for data mining botnet algorithms on large data sets. [ABSTRACT FROM AUTHOR]

In order to strengthen the overall security of the network, this paper analyzes the network security issues. The study of the network security is carried out based on the Prefix Span algorithm for data mining. The classical data mining algorithm, Prefix Span algorithm and its improvement are proposed. Then, combined with the characteristics of network security, the proposed algorithm is applied to network security intrusion detection. From the algorithm flow and evaluation model, an optimization and update scheme is proposed, and an effective data transmission evaluation model is established by effectively evaluating the status of data analysis. The experimental results show that the efficiency of the algorithm is higher than the original one. In the long sequence mode mining, the algorithm has more advantages and can better meet the high requirements of intrusion detection. However, due to the diversity and complexity of intrusion methods, data mining still needs deeper research and performance improvement in intrusion detection. [ABSTRACT FROM AUTHOR]

Due to the vast and rapid increase in data, data mining has become an increasingly important tool for the purpose of knowledge discovery in order to prevent the presence of rich data but poor knowledge. Data mining tasks can be undertaken in two ways, namely, manual walkthrough of data and use of machine learning approaches. Due to the presence of big data, machine learning has thus become a powerful tool to do data mining in intelligent ways. A popular approach of machine learning is inductive learning, which can be used to generate a rule set (a set of rules) using a particular algorithm. Inductive learning can involve a single base algorithm learning from a single data set following a standard learning approach. In this approach, the learning algorithm can generate a single rule set such as decision trees. On the other hand, the inductive learning can also involve a single base algorithm learning from multiple data sets following an ensemble learning approach. In this approach, the learning algorithm can generate multiple rule sets such as random forests. The latter approach is usually designed to reduce overfitting of models that usually arises when the former approach is adopted. In this context, the ensemble learning approach usually enables the improvement of the overall accuracy in prediction. The aim of this paper is to introduce a new approach of ensemble learning called Collaborative Rule Generation. In the new approach, the inductive learning involves multiple base algorithms learning from a single data set to generate a single rule set, which aims to enable each rule to have a higher quality. This paper also includes an experimental study validating the Collaborative Rule Generation approach and discusses the results in both quantitative and qualitative ways. [ABSTRACT FROM AUTHOR]

Community detection aims to discover cohesive groups in which people connect with each other closely in social networks. A variety of methods have been proposed to detect communities in social networks. However, there is still few work to make a comparative study on those methods. In this paper, we first introduce and compare several representative methods on community detection. Then we implement those methods with python and make a comparative analysis on different real world social networking data sets. The experimental results have shown that GN algorithm is suitable for small networks, while LPA algorithm has a better scalability. FU algorithm is of the best stability. This work could help researchers to understand the ideas of community detection methods better and select appropriate method on demand more easily. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays a crucial role to maintain healthy conditions in rotating machinery. In real industrial applications, a Machine Learning based Classifier (ML-C) analyses data from a current machinery condition to detect abnormal behaviours. Usually, this is achieved through a previous training of the ML-C model, under supervised learning; however, for new machinery conditions, the classifier is not able to correctly identify these new condition. This paper proposes a framework to detect new patterns of abnormal conditions in gearboxes, that could be associated to new faults. The framework relies on an algorithm to build evolving models in simultaneous scenarios of classification and clustering. The design is inspired by the main principles of the K-means and the One Nearest Neighbour (1-NN) algorithms. A heuristic metric is defined to analyse the new discovered clusters; as a result, these new clusters can be labelled as new classes corresponding to new faulty patterns. Once a new pattern is identified, the associated data feeds a dedicated supervised classifier which is updated through a new training phase. The proposed framework is tested on data collected from a gearbox test bed under realistic conditions of faults. Experimental results show that the algorithm is able to discover new valuable knowledge than can be identified as new faulty classes. [ABSTRACT FROM AUTHOR]

Data normalization is one of the most common processing methods applied to raw data before its subsequent use in data mining algorithms, classification, or clustering methods. Many procedures, particularly those that use any statistical analysis, require that data be normalized in one way or another. In the case of time series a standard method of processing raw data is z-normalization of each time series instance in the data set. For multivariate (multidimensional) time series we z-normalize each dimension (variable) individually. Although normalization brings a lot of advantages, it is easy to find examples of data sets where normalization destroys information contained in the raw data. In this paper we demonstrate, that for multivariate time series (MTS) both raw and normalized components give some information about the data and the best way of mining it is a combination of them. We focus here on multidimensional time series and their classification using the nearest neighbor method with the dynamic time warping (DTW) distance measure. We construct a parametric distance measure that is a combination of DTW on raw and z-normalized time series data. It turns out that the combined distance measure carries more information about the data than the two distance components separately. By determining an individual parameter for each data set it is possible to obtain a lower classification error than the errors of both component distance measures. We perform experiments on real data sets from many fields of science and technology. The advantage of the combined approach is confirmed by graphical and statistical comparisons. [ABSTRACT FROM AUTHOR]

Machine-learning and data-mining techniques have been developed to turn data into useful task-oriented knowledge. The algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level or multiple levels. Mining associations among itemsets only by using support and confidence thresholds at different levels of hierarchical data would not give interesting rules both for binary or quantitative data. This paper proposes a two phase algorithm that mines rare generalized fuzzy coherent rules at inter-cross level hierarchies. During phase-I both positive and negative fuzzy coherent rules are mined and in Phase-II, rare generalized fuzzy coherent rules are extracted from the resultant rules obtained from Phase-I. The algorithm framework works on top down methodology in generating positive and negative fuzzy coherent rules and mining rare generalized rules from it. Experiments conducted using synthetic dataset show the performance of the proposed algorithm in terms of the number of rare generalized rules generated, compared to fuzzy multiple-level association rule mining algorithm. [ABSTRACT FROM AUTHOR]

The high incidence of brain tumor has increased significantly in recent years. It is becoming more and more concernful to discover knowledge through mining medical brain image to aid doctors' diagnosis. Clustering medical images for Intelligent Decision Support is an important part in the field of medical image mining because there are several technical aspects which make this problem challenging. In this paper, we propose a medical brain image clustering method to find similar pathology images that can assist doctors to analyze the specific disease, discover its potential cause and make more accurate treatment. Firstly, this method represents medical brain image dataset as a weighted, undirected and complete graph. Secondly, this graph is sparsified so as to describe the similarity of medical images very well. Last but not the least, a graph entropy based clustering method for this sparsified graph is proposed to cluster these medical images. The experimental results show that this method can cluster medical images efficiently and run well in time complexity. The clustering results can better describe the similarity of medical images. [ABSTRACT FROM AUTHOR]

Recently, the topic of data mining has attracted considerable attention from both academia and industry. Data mining is the process of extracting useful knowledge from large amounts of data. Among the types of knowledge to be mined, classification knowledge is the most widely exploited in engineering applications. A variety of methods exist for inductive learning of "crisp" classification knowledge. This paper presents a new inductive learning algorithm called FuzzyRULES that extracts "fuzzy" classification rules from a database of examples. The use of fuzzy sets and fuzzy logic methods not only provides a powerful, flexible approach to handling vagueness and uncertainty, but also increases the expressive power and comprehensibility of the induced classification knowledge. An example involving the induction of process planning rules is used to illustrate the operation of FuzzyRULES. The algorithm has also been compared against conventional crisp and fuzzy rule induction algorithms on several benchmark data sets. The results obtained have shown that FuzzyRULES induces more compact and more accurate classification rule sets. [ABSTRACT FROM AUTHOR]

In the past, most algorithms proposed for mining association rules handle items with binary values. Transactions with quantitative values are, however, commonly seen in real-world applications. Fuzzy mining algorithms for inducing rules from quantitative databases have thus been developed, most of which are based on the Apriori algorithm. Fuzzy mining is seldom based on frequent pattern (FP) trees because fuzzy-set processing from an FP tree is much more complicated than crisp-set processing. In this paper, a two-phase fuzzy mining approach based on a tree structure to obtain fuzzy frequent itemsets from a quantitative database is proposed. A simple tree structure called the upper-bound fuzzy frequent-pattern (UBFFP) tree is designed. The two-phase fuzzy mining approach can easily derive the upper-bound fuzzy counts of itemsets using the tree. It prunes unpromising itemsets in the first phase, and then finds the actual fuzzy frequent itemsets in the second phase. Experimental results show that the proposed approach has good performance. [ABSTRACT FROM AUTHOR]

In this paper, a recurrent wavelet-based neuro-fuzzy identifier (RWNFI) with a self-organization hybrid evolution learning algorithm (SOHELA) is proposed for solving various identification problems. In the proposed SOHELA, the group-based symbiotic evolution (GSE) is adopted such that each group in the GSE represents a collection of only one fuzzy rule. The proposed SOHELA consists of structure learning and parameter learning. In structure learning, the proposed SOHELA uses the self-organization algorithm (SOA) to determine a suitable rule number in the RWNFI. In parameter learning, the proposed SOHELA uses the data mining-based selection method (DMSM) and the data mining-based crossover method (DMCM) to determine groups and parent groups using the data mining method called the frequent pattern growth (FP-Growth) method. Based on identification simulations, the excellent performance of the proposed SOHELA compares with other various existing models. [ABSTRACT FROM AUTHOR]

The only metric that had existed so far to determine the best algorithm for solving an general Asymmetric Traveling Salesman Problem (ATSP) instance is based on the number of cities; nevertheless, it is not sufficiently adequate for discriminating the best algorithm for solving an ATSP instance, thus the necessity for devising a new metric through the use of data-mining techniques. In this paper we propose: (1) the use of a genetic distance metric for improving the selection of the algorithms that best solve a given instance of the ATSP and (2) the use of discriminant analysis as a means for predictive learning (data-mining techniques) aiming at selecting meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]