A sampling-based framework for parallel mining frequent patterns

Frequent pattern mining has become an important data mining task and has been a focused theme in data mining research. Frequent patterns are patterns that appear in a data set frequently. Frequent pattern mining searches for recurring relationship in a given data set. Various techniques have been proposed to improve the performance of frequent pattern mining algorithms. This paper presents review of different frequent mining techniques including apriori based algorithms, partition based algorithms, DFS and hybrid algorithms, pattern based algorithms, SQL based algorithms and Incremental apriori based algorithms. A brief description of each technique has been provided. In the last, different frequent pattern mining techniques are compared based on various parameters of importance. Experimental results show that FP- Tree based approach achieves better performance.

Frequent pattern mining is one of the most researched areas of data mining and has recently received much attention from the database community. They are proved to be quite useful in the marketing and retail communities as well as other more diverse fields. This survey study aims at giving an overview of the previous researches done in the field of frequent pattern mining algorithms and other related issues available in the literature.

ACM Transactions on Knowledge Discovery from Data

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this article, an in-depth survey of the current status of parallel SPM (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state-of-the art PSPM. We re...

Advances in Pattern …, 2010

Abstract. Mining frequent itemsets in large databases is a widely used technique in Data Mining. Several sequential and parallel algorithms have been developed, although, when dealing with high data volumes, the execution of those algorithms takes more time and resources ...

Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming, 2005

The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data mining problems with broad applications. Perhaps the most efficient way to solve these problems sequentially is to apply a pattern-growth algorithm, which is a divide-and-conquer algorithm [9, 10]. In this paper, we present a framework for parallel mining frequent itemsets and sequential patterns based on the divide-and-conquer strategy of pattern growth. Then, we discuss the load balancing problem and introduce a sampling technique, called selective sampling, to address this problem. We implemented parallel versions of both frequent itemsets and sequential pattern mining algorithms following our framework. The experimental results show that our parallel algorithms usually achieve excellent speedups.

Data Mining and Knowledge Discovery, 2007

Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications.

Frequent pattern mining is an important chore in the data mining, which reduces the complexity of the data mining task. The usages of frequent patterns in various verticals of the data mining functionalities are discussed in this paper. The gap analysis between the requirements and the existing technology is also analyzed. State of art in the area of frequent pattern mining was thrashed out here. Working mechanisms and the usage of frequent patterns in various practices were conversed in the paper. The core area to be concentrated is the minimal representation, contextual analysis and the dynamic identification of the frequent patterns.

Frequent pattern mining is crucial part of association rule mining and other data mining tasks with many practical applications. Current popular algorithms for frequent pattern mining perform differently: some are good for dense databases while the others are ideal for sparse ones. In our previous research, we developed a new frequent pattern mining algorithm named FEM that runs fast on both sparse and dense databases. FEM combines the mining strategies of FP-growth and Eclat and given a user-specified threshold it adapts its mining behaviors to the data characteristics to efficiently find all short and long patterns from different database types. However, for best performance of FEM, an appropriate threshold value used to control the switching between its two mining tasks need to be selected by the user. In this paper, we present DFEM, an improved algorithm of FEM that automatically adopts a runtime dynamic threshold to better fit to the characteristics of the databases. The experi...

IEEE Access, 2017

Mining frequent closed sequential pattern (FCSPs) has attracted a great deal of research attention, because it is an important task in sequences mining. In recently, many studies have focused on mining frequent closed sequential patterns because, such patterns have proved to be more efficient and compact than frequent sequential patterns. Information can be fully extracted from frequent closed sequential patterns. In this paper, we propose an efficient parallel approach called parallel dynamic bit vector frequent closed sequential patterns (pDBV-FCSP) using multi-core processor architecture for mining FCSPs from large databases. The pDBV-FCSP divides the search space to reduce the required storage space and performs closure checking of prefix sequences early to reduce execution time for mining frequent closed sequential patterns. This approach overcomes the problems of parallel mining such as overhead of communication, synchronization, and data replication. It also solves the load balance issues of the workload between the processors with a dynamic mechanism that redistributes the work, when some processes are out of work to minimize the idle CPU time. INDEX TERMS Data mining, dynamic bit vectors, dynamic load balancing, multi-core processors, closed sequential patterns.

Procedia Computer Science, 2014

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm's strengths and weaknesses for finding patterns among large item sets in database systems.

Knowledge and Information Systems, 2002

Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass making it more efficient to mine all frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing dataset. Our empirical evaluation shows that the algorithm outperforms Apriori by one order of magnitude and is faster than FP-tree.

2018

Association rule mining is one of the imperative errands in data mining. The undertaking to locate the frequent patterns is assuming a fundamental part in mining associations and numerous other intriguing highlights among the factors in the transactional database. In any case, this assignment is computationally escalated and utilizes a significant extensive measure of memory. There are numerous components that include the working of a frequent pattern mining algorithm. One of the variables that have a noteworthy impact is the attributes of the database being examined. The well known algorithm works distinctively on inadequate and thick database. Two algorithms are being connected to the database as indicated by the data attributes of the dataset. FEM(FP-Tree and Eclat Method) utilizes a settled edge as an exchanging condition between the two mining techniques while DFEM(Dynamic FP-Tree and Eclat Method) applies an edge dynamically at runtime to efficiently fit the qualities of the database amid the mining procedure. The execution

Journal of Emerging Technologies and Innovative Research, 2019

Data mining involves identification of important trends or patterns through huge amounts of data. Advanced statistical techniques such as cluster analysis, artificial intelligence and neural network techniques are used in the data analysis processes. Data mining helps in better analysis of geographical data, Genome and medical sector. Classification is used for predicting outcomes and association is used to find rules affiliated with items having co-occurrence. Frequent Itemset Mining (FIM) is an approach to discover association rules in datasets. Frequent Pattern Mining (FPM) is used for finding relationships among the items in a large database obtained from the cloud environment. Association rule mining is applied for obtaining the frequent patterns. Association rule mining and frequent itemset mining are two popular and widely studied data analysis techniques for a wide range of applications such as market basket analysis, healthcare, web usage mining, bioinformatics, personalized recommendation, network optimization, medical diagnosis. This paper reviews different frequent pattern mining algorithms with weighted, interesting pattern and uncertain databases. A brief comparison of various mining algorithms based on their metrics, dataset , inferences of their work with few drawbacks were summarized. According to the reviewed papers, it was observed that uncertain database requires larger storage space and it was a time consuming process. Moreover, various challenges include checking accuracy and efficiency with time bound, setting the threshold criteria, choosing the appropriate datastructure and number of transactions containing the itemset. IndexTerms-Frequent Pattern Mining, uncertain databases, Weighted frequent itemset mining, interesting patterns, BFIforest.

IOSR Journal of Computer Engineering, 2013

Efficient algorithm to discover frequent pattern are crucial in data mining research. Finding frequent itemsets is computationally the most expensive step in association rule discovery .To address these issues we discuss popular techniques for finding frequent itemsets in efficient way. In this paper we provide the survey list of existing frequent itemsets mining techniques and proposing new procedure which having some advantages by comparing with the other algorithms.

2011

Due to rising importance in frequent pattern mining in the field of data mining research, tremendous progress has been observed in fields ranging from frequent itemset mining in transaction databases to numerous research frontiers. An elaborative note on current condition in frequent pattern mining and potential research directions is discussed in this article. It’s a strong belief that with considerably increasing research in frequent pattern mining in data analysis, it will provide a strong foundation for data mining methodologies and its applications which might prove a milestone in data mining applications in mere future. FrequentPatternMiningWithClosenessConsiderationsCurrentStateOfTheArt Strictly as per the compliance and regulations of: Global Journal of Computer Science and Technology Volume 11 Issue 17 Version 1.0 October 2011 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350 ...