OSDM: Optimized Shape Distribution Method

Ashkan Sami, Ryoichi Nagatomi, Makoto Takahashi, Takeshi Tokuyama

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

Abstract

Comprehensibility is vital in results of medical data mining systems since doctors simply require it. Another important issue specific to some data sets, like Fitness, is their uniform distribution due to tile analysis that was performed on them. In this paper, we propose a novel data mining tool named OSDM (Optimized Shape Distribution Method) to give a comprehensive view of correlations of attributes in cases of uneven frequency distribution among different values of symptoms. We apply OSDM to explore the relationship of the Fitness data and symptoms in medical test dataset for which popular data mining methods fail to give an appropriate output to help doctors decisions. In our experiment, OSDM found several useful relationships.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - Second International Conference, ADMA 2006, Proceedings
EditorsXue Li, Osmar R. Zaïane, Zhanhuai Li
PublisherSpringer Verlag
Pages1057-1064
Number of pages8
ISBN (Print)3540370250, 9783540370253
DOIs
Publication statusPublished - 2006
Event2nd International Conference on Advanced Data Mining and Applications, ADMA 2006 - Xi'an, China
Duration: 2006 Aug 142006 Aug 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4093 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Advanced Data Mining and Applications, ADMA 2006
CountryChina
CityXi'an
Period06/8/1406/8/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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