TreeITL-mine: Mining frequent itemsets using pattern growth, tid intersection, and prefix tree

Raj P. Gopalan, Yudho Giri Sucahyo

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

5 Citations (Scopus)

Abstract

An important problem in data mining is the discovery of association rules that identify relationships among sets of items. Finding frequent itemsets is computationally the most expensive step in association rules mining, and so most of the research attention has been focused on it. In this paper, we present a more efficient algorithm for mining frequent itemsets. In designing our algorithm, we have combined the ideas of pattern-growth, tid-intersection and prefix trees, with significant modifications. We present performance comparisons of our algorithm against the fastest Apriori algorithm, and the recently developed H-Mine algorithm. We have tested all the algorithms using several widely used test datasets. The performance results indicate that our algorithm significantly reduces the processing time for mining frequent itemsets in dense data sets that contain relatively long patterns.

Original languageEnglish
Title of host publicationAI 2002
Subtitle of host publicationAdvances in Artificial Intelligence - 15th Australian Joint Conference on Artificial Intelligence, Proceedings
EditorsBob McKay, John Slaney
PublisherSpringer Verlag
Pages535-546
Number of pages12
ISBN (Print)3540001972, 9783540001973
Publication statusPublished - 1 Jan 2002
Event15th Australian Joint Conference on Artificial Intelligence, AI 2002 - Canberra, Australia
Duration: 2 Dec 20026 Dec 2002

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2557
ISSN (Print)0302-9743

Conference

Conference15th Australian Joint Conference on Artificial Intelligence, AI 2002
Country/TerritoryAustralia
CityCanberra
Period2/12/026/12/02

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