A Hybrid Method for High-Utility Itemsets Mining in Large High-Dimensional Data

A Hybrid Method for High-Utility Itemsets Mining in Large High-Dimensional Data

Guangzhu Guangzhu Yu (Donghua University, China), Shihuang Shao (Donghua University, China), Bin Luo (Guangdong University of Technology, China) and Xianhui Zeng (Donghua University, China)
Copyright: © 2009 |Pages: 17
DOI: 10.4018/jdwm.2009010104
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Existing algorithms for high-utility itemsets mining are column enumeration based, adopting an Apriorilike candidate set generation-and-test approach, and thus are inadequate in datasets with high dimensions or long patterns. To solve the problem, this paper proposed a hybrid model and a row enumeration-based algorithm, i.e., Inter-transaction, to discover high-utility itemsets from two directions: an existing algorithm can be used to seek short high-utility itemsets from the bottom, while Inter-transaction can be used to seek long high-utility itemsets from the top. Inter-transaction makes full use of the characteristic that there are few common items between or among long transactions. By intersecting relevant transactions, the new algorithm can identify long high-utility itemsets, without extending short itemsets step by step. In addition, we also developed new pruning strategies and an optimization technique to improve the performance of Inter-transaction.

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