Personalized knowledge discovery: Mining novel association rules from text

Xin Chen, Yi Fang Wu

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

6 Scopus citations

Abstract

This paper presents a methodology for personalized knowledge discovery from text. It derives a user's background knowledge from his/her background documents, and exploits such knowledge to evaluate the novelty of discovered knowledge in the form of association rules by measuring the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment results show that the proposed user-oriented novelty measure is highly correlated with the human subjective rule novelty and usefulness ratings. It outperforms seven major objective interestingness measures and the WordNet novelty measure for identifying novel and useful rules.

Original languageEnglish (US)
Title of host publicationProceedings of the Sixth SIAM International Conference on Data Mining
PublisherSociety for Industrial and Applied Mathematics
Pages589-593
Number of pages5
ISBN (Print)089871611X, 9780898716115
DOIs
StatePublished - 2006
EventSixth SIAM International Conference on Data Mining - Bethesda, MD, United States
Duration: Apr 20 2006Apr 22 2006

Publication series

NameProceedings of the Sixth SIAM International Conference on Data Mining
Volume2006

Other

OtherSixth SIAM International Conference on Data Mining
CountryUnited States
CityBethesda, MD
Period4/20/064/22/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Association Rule Mining
  • Interestingness
  • Novelty
  • Personalization
  • Text Mining

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