Data Mining: Understanding Data and Disease Modeling

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TypeArticle
ConferenceProceedings of IASTED-AI-03 Conference, February 10-13, 2003., Innsbruck, Austria
Subjectdata mining; functional genomics; disease modeling; bioinformatics
AbstractAnalyzing large data sets requires proper understanding of the data in advance. This would help domain experts to influence the data mining process and to properly evaluate the results of a data mining application. In this paper, we introduce an algorithm to identify anomalies in the data. We also propose an approach to include the results of data characteristics checking in a data mining application. The application, reported in this paper, involves developing a disease model from gene expression data using machine learning techniques. We demonstrate how: (i) simple models can be generated from a large set of attributes and (ii) the structure of the models change, when potentially anomalous cases are removed.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number45789
NPARC number8913255
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Record identifiere4c8b611-9770-4f2f-a23d-0ba5b3e54bca
Record created2009-04-22
Record modified2016-05-09
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