A new method for mining Frequent Weighted Itemsets based on WIT-trees

Authors:
Bay Vo;Frans Coenen;Bac Le
Affiliations:
Department of Computer Science, Information Technology College, Ho Chi Minh, Viet Nam;Department of Computer Science, University of Liverpool, UK;Department of Computer Science, University of Science, Ho Chi Minh, Viet Nam
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

The mining frequent itemsets plays an important role in the mining of association rules. Frequent itemsets are typically mined from binary databases where each item in a transaction may have a different significance. Mining Frequent Weighted Itemsets (FWI) from weighted items transaction databases addresses this issue. This paper therefore proposes algorithms for the fast mining of FWI from weighted item transaction databases. Firstly, an algorithm for directly mining FWI using WIT-trees is presented. After that, some theorems are developed concerning the fast mining of FWI. Based on these theorems, an advanced algorithm for mining FWI is proposed. Finally, a Diffset strategy for the efficient computation of the weighted support for itemsets is described, and an algorithm for mining FWI using Diffsets presented. A complete evaluation of the proposed algorithms is also presented.