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

Hyeoncheol Kim; Eun Young Kwak

doi:10.1007/11552413_54

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 proceeding › Conference 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 language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Publisher	Springer Verlag
Pages	372-378
Number of pages	7
ISBN (Print)	3540288945, 9783540288947
DOIs	https://doi.org/10.1007/11552413_54
Publication status	Published - 2005
Event	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia Duration: 2005 Sept 14 → 2005 Sept 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3681 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Country/Territory	Australia
City	Melbourne
Period	05/9/14 → 05/9/16

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/11552413_54

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

Information-based pruning for interesting association rule mining in the item response dataset. / Kim, Hyeoncheol; Kwak, Eun Young.
Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. p. 372-378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3681 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, H & Kwak, EY 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3681 LNAI, Springer Verlag, pp. 372-378, 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, 05/9/14. https://doi.org/10.1007/11552413_54

Kim H, Kwak EY. 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. Springer Verlag. 2005. p. 372-378. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11552413_54

Kim, Hyeoncheol ; Kwak, Eun Young. / Information-based pruning for interesting association rule mining in the item response dataset. Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. pp. 372-378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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