Efficiently mining high average utility itemsets with a tree structure

Chun Wei Lin, Tzung Pei Hong, Wen-Hsiang Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Citations (Scopus)

Abstract

The average utility measure has recently been proposed to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. In this paper, a new mining approach with the aid of a tree structure is proposed to efficiently implement the concept. The high average utility pattern tree (HAUP tree) structure is first designed to help keep some related information and then the HAUP-growth algorithm is proposed to mine high average utility itemsets from the tree structure. Experimental results also show that the proposed approach has a better performance than the Apriori-like average utility mining.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings
Pages131-139
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2010 Sep 29
Event2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010 - Hue City, Viet Nam
Duration: 2010 Mar 242010 Mar 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5990 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010
CountryViet Nam
CityHue City
Period10-03-2410-03-26

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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