Investigation of rule interestingness in medical data mining

Miho Ohsaki, Shinya Kitaguchi, Hideto Yokoi, Takahira Yamaguchi

研究成果: Conference contribution

8 被引用数 (Scopus)

抄録

This research experimentally investigates the performance of conventional rule interestingness measures and discusses their usefulness for supporting KDD through human-system interaction in medical domain. We compared the evaluation results by a medical expert and those by selected sixteen kinds of interestingness measures for the rules discovered in a dataset on hepatitis. χ2 measure, recall, and accuracy demonstrated the highest performance, and specificity and prevalence did the lowest. The interestingness measures showed a complementary relationship for each other. These results indicated that some interestingness measures have the possibility to predict really interesting rules at a certain level and that the combinational use of interestingness measures will be useful. We then discussed how to combinationally utilize interestingness measures and proposed a post-processing user interface utilizing them, which supports KDD through human-system interaction.

本文言語English
ホスト出版物のタイトルActive Mining - Second International Workshop, AM 2003, Revised Selected Papers
出版社Springer Verlag
ページ174-189
ページ数16
ISBN(印刷版)3540261575, 9783540261575
DOI
出版ステータスPublished - 2005
イベントSecond International Workshop on Active Mining, AM 2003 - Maebashi, Japan
継続期間: 2003 10 282003 10 31

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3430 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

OtherSecond International Workshop on Active Mining, AM 2003
CountryJapan
CityMaebashi
Period03/10/2803/10/31

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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