A three-scan algorithm to mine high on-shelf utility itemsets

Guo Cheng Lan*, Tzung Pei Hong, S. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In this paper, we handle a new kind of patterns named high on-shelf utility itemsets, which considers not only individual profit and quantity of each item in a transaction but also common on-shelf time periods of a product combination. We propose a three-scan mining approach to effectively and efficiently discover high on-shelf utility itemsets. The proposed approach adopts an itemset-generation mechanism to prune redundant candidates early and to systematically check the itemsets from transactions. The experimental results on synthetic datasets also show the proposed approach has a good performance.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings
Pages351-358
Number of pages8
EditionPART 2
DOIs
StatePublished - 17 Sep 2010
Event2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010 - Hue City, Viet Nam
Duration: 24 Mar 201026 Mar 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5991 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010
CountryViet Nam
CityHue City
Period24/03/1026/03/10

Keywords

  • data mining
  • high utility itemsets
  • on-shelf data
  • utility mining

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