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

Guangzhu Guangzhu Yu, Shihuang Shao, Bin Luo and Xianhui Zeng
Additional contact information
Guangzhu Guangzhu Yu: Donghua University, China
Shihuang Shao: Donghua University, China
Bin Luo: Guangdong University of Technology, China
Xianhui Zeng: Donghua University, China

International Journal of Data Warehousing and Mining (IJDWM), 2009, vol. 5, issue 1, 57-73

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.

Date: 2009
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/jdwm.2009010104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:5:y:2009:i:1:p:57-73

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().