Frequent Itemset Mining Techniques Research Articles

A design of cross-selling products based on frequent itemset mining for coffee shop business

Using a case study at the XYZ coffee shop in Bandar Lampung, Indonesia, this work conducts knowledge extraction on a sales dataset to create a cross-selling model for product bundling advice. Understanding Knowledge extraction was done using a frequent itemset mining technique based on an Apriori algorithm to extract a set of association rules between products. The study implemented a five-stage frequent itemset structure that encompasses business comprehension, data preparation, data exploration, model creation using the Apriori algorithm, and rules evaluation. The framework that comes from this research provides a set of bundling association rules across items for cross-selling strategies that involve many products and complex sales timeframes. Additionally, we recommended sales activities based on loyalty cards to enhance our dataset with consumer attributes and purchasing trends. Based on customized services and tailored offers based on consumers' past purchases and spending patterns, the loyalty card recommendation was created. Consequently, we may target the appropriate clients and items with our marketing initiatives

The Mining Algorithm of Maximum Frequent Itemsets Based on Frequent Pattern Tree.

In the discipline of data mining, association rule mining is an important study topic that focuses on discovering the relationships between database attributes. The maximum frequent itemset comprises the information of all frequent itemsets, which is one of the important difficulties in mining association rules, and certain data mining applications just need to mine the maximum frequent itemsets. As a result, analyzing the maximum frequent itemset mining technique is practical. Considering this, the research introduces FP-MFIA, a new maximum frequent itemset mining approach based on the FP-tree, which is inspired by the data structure of the frequent pattern tree and the idea that the maximum frequent itemset implies all frequent itemsets. First, the FP-MFIA constructs a one-way FP-tree structure, which only has pointers from the root to the leaves, so that only two scans of the FP-tree are required by the FP-MFIA. On the other hand, it redefines a data storage structure MFI-list for maximum frequent itemsets. It can quickly release unnecessary nodes in the FP-tree after scanning it. In this way, not only the information required by the maximum frequent itemsets can be quickly mined but also the space required for storing the maximum frequent itemsets can be reduced, which greatly improves the mining efficiency. Finally, experiments were conducted to compare the mining efficiency of the novel FP-MFIA algorithm to the IDMFIA and DMFIA algorithms. We can see from the findings that the FP-MFIA algorithm is more efficient than the other two techniques.

A Method of Monitoring and Evaluating the Quality of English Classroom Teaching Based on Big Data

To improve the reliability and accuracy of quality of English instruction in the classroom monitoring and assessment, as well as to reduce the time spent monitoring and evaluating the quality of English instruction in the classroom, a big data-based approach to monitor and evaluate the quality of English instruction in the classroom is proposed. A frequent itemset mining technique for quality of English instruction in the classroom monitoring data is built based on a study of the theoretical basis of big data and the features of quality of English instruction in the classroom monitoring data. To complete the data transformation of quality of English instruction in the classroom monitoring, the multivalued continuous attribute is quantified and turned into a two-dimensional Boolean data matrix. To mine the frequent itemsets of quality of English instruction in the classroom monitoring data, a frequent itemset mining algorithm based on the compressed matrix is applied. This work creates a quality of English instruction in the classroom evaluation model using the gray correlation method of multiobjective decision-making and weighted gray correlation analysis to realize the monitoring and evaluation of quality of English instruction in the classroom. The experimental results suggest that the proposed method is highly reliable and accurate and that it can significantly reduce the time spent monitoring and evaluating the quality of English classroom instruction.

Mining Temporal Association Rules with Temporal Soft Sets

Traditional association rule extraction may run into some difficulties due to ignoring the temporal aspect of the collected data. Particularly, it happens in many cases that some item sets are frequent during specific time periods, although they are not frequent in the whole data set. In this study, we make an effort to enhance conventional rule mining by introducing temporal soft sets. We define temporal granulation mappings to induce granular structures for temporal transaction data. Using this notion, we define temporal soft sets and their Q -clip soft sets to establish a novel framework for mining temporal association rules. A number of useful characterizations and results are obtained, including a necessary and sufficient condition for fast identification of strong temporal association rules. By combining temporal soft sets with NegNodeset-based frequent item set mining techniques, we develop the negFIN-based soft temporal association rule mining (negFIN-STARM) method to extract strong temporal association rules. Numerical experiments are conducted on commonly used data sets to show the feasibility of our approach. Moreover, comparative analysis demonstrates that the newly proposed method achieves higher execution efficiency than three well-known approaches in the literature.

TKFIM: Top-K frequent itemset mining technique based on equivalence classes.

Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset’s characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.

Minimizing information loss in shared data: Hiding frequent patterns with multiple sensitive support thresholds

AbstractPrivacy preserving data mining (PPDM) is the process of protecting sensitive knowledge from being discovered by data mining techniques in case of data sharing. Privacy preserving frequent itemset mining (PPFIM) is a subtask and NP‐hard problem of PPDM. Its objective is to modify a given database in such a way that none of the sensitive itemsets of the database owner can be obtained by any frequent itemset mining technique from the modified database. The main challenge of PPFIM is to minimize the distortion given to the data and nonsensitive knowledge while sanitizing all given sensitive itemsets. Distortion‐based sensitive itemset hiding algorithms decrease the support of each sensitive itemset under a predefined sensitive threshold through sanitization. Most of the distortion‐based itemset hiding algorithms allow database owner to define a single sensitive threshold for each sensitive itemset. However, this is a limitation to the database owner since the importance of each sensitive itemset varies. In this paper we propose a distortion‐based itemset hiding algorithm that allows database owner to assign multiple sensitive thresholds, namely itemset oriented pseudo graph based sanitization (IPGBS) algorithm. The purpose of IPGBS algorithm is to give minimum distortion to the nonsensitive knowledge and data while hiding all sensitive itemsets. For this reason, the IPGBS algorithm modifies least amount of transaction and transaction content. The performance evaluation of the IPGBS algorithm is conducted by using two different counterparts on four different databases. The results show that the IPGBS algorithm is more efficient in terms of nonsensitive frequent itemset loss on both dense and sparse databases. It has considerable good results in terms of number of transactions modified, number of items deleted, execution time and total memory allocation as well.

Associative classification based on the Transferable Belief Model

Associative classification is, in essence, a frequent itemset mining technique designed specifically for classification tasks. It can be viewed as a second-order data mining problem, where the aim is to obtain an accurate classifier taking the mined set formed by class association rules (CARs) as raw material; that is, association rules in which only the class attribute is considered in the rule’s consequent. Traditionally, the classifier is obtained from the initial CAR−set applying heuristic methods for sorting and pruning, leading to a (relatively small) set of classification rules. In this paper, instead of using heuristic-based methods, we propose the use of the Transferable Belief Model for obtaining an understandable, accurate and compact classifier composed of (pignistic) probability functions that summarize the huge mined set of rules. The experimental results obtained with benchmark datasets show the effectiveness of our promising proposal.

Co-prescription patterns of cardiovascular preventive treatments: a cross-sectional study in the Aragon worker’ health study (Spain)

ObjectivesTo identify cardiovascular disease (CVD) preventive treatments combinations, among them and with other drugs, and to determine their prevalence in a cohort of Spanish workers.DesignCross-sectional study.SettingAragon Workers’ Health Study (AWHS)...

Extracting useful knowledge from event logs: A frequent itemset mining approach

Business process analysis is a key activity that aims at increasing the efficiency of business operations. In recent years, several data mining based methods have been designed for discovering interesting patterns in event logs. A popular type of methods consists of applying frequent itemset mining to extract patterns indicating how resources and activities are frequently used. Although these methods are useful, they have two important limitations. First, these methods are designed to be applied to original event logs. Because these methods do not consider other perspectives on the data that could be obtained by applying data transformations, many patterns are missed that may represent important information for businesses. Second, these methods can generate a large number of patterns since they only consider the minimum support as constraint to select patterns. But analyzing a large number of patterns is time-consuming for users, and many irrelevant patterns may be found. To address these issues, this paper presents an improved event log analysis approach named AllMining. It includes a novel pre-processing method to construct multiple types of transaction databases from a same original event log using transformations. This allows to extract many new useful types of patterns from event logs with frequent itemset mining techniques. To address the second issue, a pruning strategy is further developed based on a novel concept of pattern coverage, to present a small set of patterns that covers many events to decision makers. Results of experiments on real-life event logs show that the proposed approach is promising compared to existing frequent itemset mining approaches and state-of-the-art process model algorithms.

A study of frequent itemset mining techniques

Frequent item set is the most crucial and expensive task for the industry today. It is the task of mining the information from different sources and a key approach in Data Mining. Frequent item sets satisfying the minimum threshold can be discovered. Association rules are extracted from frequent item sets. The Association rules are affected by the minimum support value entered by the user may be considered as Positive or negative. There may be some other Association rules, which involve the rare item sets. Various methods have been used by researchers for generating the Association Rules. In this paper, our aim is to study various techniques to generate the Association rules.

Frequent Itemset Mining in Large Datasets a Survey

Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules. The amount of data is increasing day by day taking form of BigData, which require changes in the algorithms for working on such large-scale data. Parallel implementation of the mining techniques can provide solutions to this problem. In this paper a survey of frequent itemset mining techniques is done which can be used in a parallel environment. Programming models like Map Reduce provides efficient architecture for working with BigData, paper also provides information about issues and feasibility about technique to be implemented in such environment.

Multiobjective Adaptive Fuzzy C-Means Algorithm for Mining Frequent Closed Itemset: A Lattice Based Approach for Time Series Data

Of late, frequent itemset mining from the data bases has emerged as a highly challenging task. And, there is a feast of various approaches launched with an eye on extracting the frequent rules from the database. In the erstwhile frequent itemset mining technique, the frequent itemset are extracted from the time series data by means of Adaptive Fuzzy C-Means (AFCM) clustering, though it is saddled with several setbacks in the clustering procedure. This arises on account of the fact that in clustering, the number of itemset is mammoth, and hence the method consumes a lot of time in addition to spending enhanced scanning time. Therefore, with a view tackling these adverse features, an innovative frequent itemset mining method is suggested in this document. At the outset, the time sequence database values are grouped by means of the Multiobjective AFCM method. Subsequently, the frequent itemset are extracted from the grouped outcomes by employing the lattice based method. The novel technique is performed in the MATLAB platform and its excellence is appraised by means of rainfall database.

트랜잭션 가중치 기반의 빈발 아이템셋 마이닝 기법의 성능분석

트랜잭션 가중치 기반의 빈발 아이템셋 마이닝 기법의 성능분석

Modern Association Rule Mining Methods

Mining academic social network is becoming increasingly necessary with the increasing amount of data. It is a favorite topic of research for many researchers. The data mining techniques are used for the mining of academic social networks. In this paper, we are presenting an efficient frequent item set mining technique for social academic network. The proposed framework first processes the research documents and then the enhanced frequent item set mining is applied to find the strength of relationship between the researchers. The proposed method will be fast in comparison to older algorithms. Also it will takes less main memory space for computation purpose.

Unravelling associations between unassigned mass spectrometry peaks with frequent itemset mining techniques.

BackgroundMass spectrometry-based proteomics experiments generate spectra that are rich in information. Often only a fraction of this information is used for peptide/protein identification, whereas a significant proportion of the peaks in a spectrum remain unexplained. In this paper we explore how a specific class of data mining techniques termed “frequent itemset mining” can be employed to discover patterns in the unassigned data, and how such patterns can help us interpret the origin of the unexpected/unexplained peaks.ResultsFirst a model is proposed that describes the origin of the observed peaks in a mass spectrum. For this purpose we use the classical correlative database search algorithm. Peaks that support a positive identification of the spectrum are termed explained peaks. Next, frequent itemset mining techniques are introduced to infer which unexplained peaks are associated in a spectrum. The method is validated on two types of experimental proteomic data. First, peptide mass fingerprint data is analyzed to explain the unassigned peaks in a full scan mass spectrum. Interestingly, a large numbers of experimental spectra reveals several highly frequent unexplained masses, and pattern mining on these frequent masses demonstrates that subsets of these peaks frequently co-occur. Further evaluation shows that several of these co-occurring peaks indeed have a known common origin, and other patterns are promising hypothesis generators for further analysis. Second, the proposed methodology is validated on tandem mass spectrometral data using a public spectral library, where associations within the mass differences of unassigned peaks and peptide modifications are explored. The investigation of the found patterns illustrates that meaningful patterns can be discovered that can be explained by features of the employed technology and found modifications.ConclusionsThis simple approach offers opportunities to monitor accumulating unexplained mass spectrometry data for emerging new patterns, with possible applications for the development of mass exclusion lists, for the refinement of quality control strategies and for a further interpretation of unexplained spectral peaks in mass spectrometry and tandem mass spectrometry.Electronic supplementary materialThe online version of this article (doi:10.1186/s12953-014-0054-1) contains supplementary material, which is available to authorized users.

Beyond itemsets

We assume a dataset of transactions generated by a set of users over structured items where each item could be described through a set of features. In this paper, we are interested in identifying the frequent featuresets (set of features) by mining item transactions. For example, in a news website, items correspond to news articles, the features are the named-entities/topics in the articles and an item transaction would be the set of news articles read by a user within the same session. We show that mining frequent featuresets over structured item transactions is a novel problem and show that straightforward extensions of existing frequent itemset mining techniques provide unsatisfactory results. This is due to the fact that while users are drawn to each item in the transaction due to a subset of its features, the transaction by itself does not provide any information about such underlying preferred features of users. In order to overcome this hurdle, we propose a featureset uncertainty model where each item transaction could have been generated by various featuresets with different probabilities. We describe a novel approach to transform item transactions into uncertain transaction over featuresets and estimate their probabilities using constrained least squares based approach. We propose diverse algorithms to mine frequent featuresets. Our experimental evaluation provides a comparative analysis of the different approaches proposed.

A primer to frequent itemset mining for bioinformatics

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.

Representation and querying of unfair evaluations in social rating systems

Social rating systems are subject to unfair evaluations. Users may try to individually or collaboratively promote or demote a product. Detecting unfair evaluations, mainly massive collusive attacks as well as honest looking intelligent attacks, is still a real challenge for collusion detection systems. In this paper, we study the impact of unfair evaluations in online rating systems. First, we study the individual unfair evaluations and their impact on the reputation of people calculated by social rating systems. We then propose a method for detecting collaborative unfair evaluations, also known as collusion. The proposed model uses frequent itemset mining technique to detect the candidate collusion groups and sub-groups. We use several indicators to identify collusion groups and to estimate how destructive such colluding groups can be. The approaches presented in this paper have been implemented in prototype tools, and experimentally validated on synthetic and real-world datasets.

Linkage graph analysis: A linkage-group-based QTL synthesis analysis approach

Consensus quantitative trait loci (QTL) in meta-analysis of multiple independent QTL mapping experiments provides a strong foundation for marker-assisted selection and gene cloning. However, meta-analysis suffers from the lack of available genomic information and the results vary when different reference linkage maps are used. Here, to overcome these limitations, we propose a linkage-group-based QTL synthesis analysis approach that we have named linkage graph analysis. First, a graph model is constructed from derived linkage groups. Next, an unsupervised classification approach is used to obtain marker intervals with co-segregating patterns among multiple genomes. Finally, a frequent itemset mining technique is used to identify the markers (or intervals) closely linked to the QTL. The proposed method was validated by one Monte Carlo simulation study and by real data analysis of cotton genomes. Two major advantages of the new method are: (i) A reference linkage group is not required; (ii) the effect of the initial QTL is reduced because false QTLs can be detected and excluded from the dataset. The ability to reliably identify the markers associated with a true QTL is valuable in crop breeding.

Detecting higher-level similarity patterns in programs

Cloning in software systems is known to create problems during software maintenance. Several techniques have been proposed to detect the same or similar code fragments in software, so-called simple clones . While the knowledge of simple clones is useful, detecting design-level similarities in software could ease maintenance even further, and also help us identify reuse opportunities. We observed that recurring patterns of simple clones - so-called structural clones - often indicate the presence of interesting design-level similarities. An example would be patterns of collaborating classes or components. Finding structural clones that signify potentially useful design information requires efficient techniques to analyze the bulk of simple clone data and making non-trivial inferences based on the abstracted information. In this paper, we describe a practical solution to the problem of detecting some basic, but useful, types of design-level similarities such as groups of highly similar classes or files. First, we detect simple clones by applying conventional token-based techniques. Then we find the patterns of co-occurring clones in different files using the Frequent Itemset Mining (FIM) technique. Finally, we perform file clustering to detect those clusters of highly similar files that are likely to contribute to a design-level similarity pattern. The novelty of our approach is application of data mining techniques to detect design level similarities. Experiments confirmed that our method finds many useful structural clones and scales up to big programs. The paper describes our method for structural clone detection, a prototype tool called Clone Miner that implements the method and experimental results.