Information-based pruning for interesting association rule mining in the item response dataset

Hyeoncheol Kim, Eun Young Kwak

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

6 Citations (Scopus)

Abstract

Frequency-based mining of association rules sometimes suffers rule quality problems. In this paper, we introduce a new measure called surprisal that estimates the informativeness of transactional instances and attributes. We eliminate noisy and uninformative data using the surprisal first, and then generate association rules of good quality. Experimental results show that the surprisal-based pruning improves quality of association rules in question item response datasets significantly.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
PublisherSpringer Verlag
Pages372-378
Number of pages7
ISBN (Print)3540288945, 9783540288947
DOIs
Publication statusPublished - 2005
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: 2005 Sep 142005 Sep 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3681 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
CountryAustralia
CityMelbourne
Period05/9/1405/9/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, H., & Kwak, E. Y. (2005). Information-based pruning for interesting association rule mining in the item response dataset. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 372-378). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3681 LNAI). Springer Verlag. https://doi.org/10.1007/11552413_54