Top down FP-growth for association rule mining

Ke Wang, Liu Tang, Jiawei Han, Junqiang Liu

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

Abstract

In this paper, we propose an efficient algorithm, called TD-FP- Growth (the shorthand for Top-Down FP-Growth), to mine frequent patterns. TD-FP-Growth searches the FP-tree in the top-down order, as opposed to the bottom-up order of previously proposed FP-Growth. The advantage of the topdown search is not generating conditional pattern bases and sub-FP-trees, thus, saving substantial amount of time and space. We extend TD-FP-Growth to mine association rules by applying two new pruning strategies: one is to push multiple minimum supports and the other is to push the minimum confidence. Experiments show that these algorithms and strategies are highly effective in reducing the search space.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 6th Pacific-Asia Conference, PAKDD 2002, Proceedings
EditorsMing-Syan Chen, Philip S. Yu, Bing Liu
PublisherSpringer-Verlag Berlin Heidelberg
Pages334-340
Number of pages7
ISBN (Print)9783540437048
DOIs
StatePublished - 2002
Externally publishedYes
Event6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002 - Taipei, Taiwan, Province of China
Duration: May 6 2002May 8 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2336
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2002
CountryTaiwan, Province of China
CityTaipei
Period5/6/025/8/02

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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