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

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Author(s): 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: 2011
Pages: 17
Source title: Integrations of Data Warehousing, Data Mining and Database Technologies: Innovative Approaches
Source Author(s)/Editor(s): David Taniar (Monash University, Australia)and Li Chen (LaTrobe University, Australia)
DOI: 10.4018/978-1-60960-537-7.ch004

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

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

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