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

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 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.

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 17: 4 Issues (2021): Forthcoming, Available for Pre-Order
Volume 16: 4 Issues (2020): 3 Released, 1 Forthcoming
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing