An algorithm for constrained association rule mining in semi-structured data

Lisa Singh, Bin Chen, Rebecca Haight, Peter Scheuermann

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

8 Scopus citations

Abstract

The need for sophisticated analysis of textual documents is becoming more apparent as data is being placed on the Web and digital libraries are surfacing. This paper presents an algorithm for generating constrained association rules from textual documents. The user species a set of constraints, concepts and/or structured values. Our algorithm creates matrices and lists based on these prespecied constraints and uses them to generate large itemsets. Because these matrices are small and sparse, we are able to quickly generate higher order large itemsets. Further, since we maintain concept relationship information in a concept library, we can also generate rulesets involving concepts related to the initial set of constraints.

Original languageEnglish (US)
Title of host publicationMethodologies for Knowledge Discovery and Data Mining - 3rd Pacific-Asia Conference, PAKDD 1999, Proceedings
EditorsNing Zhong, Lizhu Zhou
PublisherSpringer Verlag
Pages148-158
Number of pages11
ISBN (Print)3540658661, 9783540658665
DOIs
StatePublished - 1999
Event3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1999 - Beijing, China
Duration: Apr 26 1999Apr 28 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1574
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1999
Country/TerritoryChina
CityBeijing
Period4/26/994/28/99

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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