Using an interest ontology for improved support in rule mining

Xiaoming Chen, Xuan Zhou, Richard Scherl, James Geller

Research output: Chapter in Book/Report/Conference proceedingChapter

11 Scopus citations

Abstract

This paper describes the use of a concept hierarchy for improving the results of association rule mining. Given a large set of tuples with demographic information and personal interest information, association rules can be derived, that associate ages and gender with interests. However, there are two problems. Some data sets are too sparse for coming up with rules with high support. Secondly, some data sets with abstract interests do not represent the actual interests well. To overcome these problems, we are preprocessing the data tuples using an ontology of interests. Thus, interests within tuples that are very specific are replaced by more general interests retrieved from the interest ontology. This results in many more tuples at a more general level. Feeding those tuples to an association rule miner results in rules that have better support and that better represent the reality.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsYahiko Kambayashi, Mukesh Mohania, Wolfram Wob
PublisherSpringer Verlag
Pages320-329
Number of pages10
ISBN (Print)354040807X
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2737
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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