Visualizing discovered rule sets with visual graphs based on compressed entropy density

Einoshin Suzuki, Hiroki Ishihara

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

1 Citation (Scopus)

Abstract

This paper presents a post-processing algorithm of rule discovery for augmenting the readability of a discovered rule set. Rule discovery, in spite of its usefulness as a fundamental data-mining technique, outputs a huge number of rules. Since usefulness of a discovered rule is judged by human inspection, augmenting the readability of a discovered rule set is an important issue. We formalize this problem as a transformation of a rule set into a tree structure called a visual graph. A novel information-based criterion which represents compressed entropy of a data set per description length of the graph is employed in order to evaluate the readability quantitatively. Experiments with an agricultural data set in cooperation with domain experts confirmed the effectiveness of our method in terms of readability and validness.

Original languageEnglish
Title of host publicationNew Directions in Rough Sets, Data Mining, and Granular-Soft Computing - 7th International Workshop, RSFDGrC 1999, Proceedings
EditorsSetsuo Ohsuga, Ning Zhong, Andrzej Skowron
PublisherSpringer Verlag
Pages414-423
Number of pages10
ISBN (Print)3540666451, 9783540666455
DOIs
Publication statusPublished - Jan 1 1999
Externally publishedYes
Event7th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, RSFDGrC 1999 - Yamaguchi, Japan
Duration: Nov 9 1999Nov 11 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1711
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, RSFDGrC 1999
CountryJapan
CityYamaguchi
Period11/9/9911/11/99

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Suzuki, E., & Ishihara, H. (1999). Visualizing discovered rule sets with visual graphs based on compressed entropy density. In S. Ohsuga, N. Zhong, & A. Skowron (Eds.), New Directions in Rough Sets, Data Mining, and Granular-Soft Computing - 7th International Workshop, RSFDGrC 1999, Proceedings (pp. 414-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1711). Springer Verlag. https://doi.org/10.1007/978-3-540-48061-7_50