Enhanced Index-Based GenMax for Frequent Item Set Mining

Enhanced Index-Based GenMax for Frequent Item Set Mining

S. Asokkumar, S. Thangavel
Copyright: © 2014 |Pages: 11
DOI: 10.4018/ijgc.2014010101
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In many data mining applications such as the discovery of association rules, strong rules, and many other important discovery tasks, mining frequent item sets is a fundamental and essential problem. Methods have been implemented for mining frequent item sets using a prefix-tree structure, for storing compressed information GenMax is used for mining maximal frequent item sets. It uses a technique called progressive focusing to perform maximal checking, and differential set propagation to perform fast frequency computation. GenMax algorithm was not implemented for closed frequent item set. The proposal in this paper present an improved index based enhancement on Genmax algorithm for effective fast and less memory utilized pruning of maximal frequent item and closed frequent item sets. The extension induces a search tree on the set of frequent closed item sets thereby we can completely enumerate closed item sets without duplications. The memory use of mining the maximal frequent item set does not depend on the number of frequent closed item sets. The proposed model reduces the number of disk (Input and Outputs) I/Os and make frequent item set mining scale to large transactional databases. Experimental results shows a comparison of improved index based GenMax and existing GenMax for efficient pruning of maximal frequent and closed frequent item sets in terms of item precision and fastness.
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Literature Review And Problem Statement

Mining of association rules from large data sets has been a focused topic in recent data mining research (Grahne and J. Zhu, 2004). To explore multiple-level association rule mining, (Leung et al., 2002) one needs to provide i.e., data at multiple levels of abstraction, and efficient methods for multiple-level rule mining. There are several possible directions to explore efficient mining of multiple-level association rules (Liu et al., 2003). The fundamental problem in item set mining is formulated as follows: Many of the proposed itemset mining algorithms are a variant of Apriori (Faloutsos et al., 1995), which employs a bottom-up, breadth-first search that enumerates every single frequent itemset. In many applications (especially in dense data) with long frequent patterns enumerating all possible 2m-2 subsets of a m length pattern (m can easily be 30 or 40 or longer) is computationally unfeasible (Baralis et al., 2005).

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