| Download | - View accepted manuscript: Data Mining: Understanding Data and Disease Modeling (PDF, 355 KiB)
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| Author | Search for: Famili, Fazel; Search for: Ouyang, Junjun |
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| Format | Text, Article |
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| Conference | IASTED-AI-03 Conference, February 10-13, 2003, Innsbruck, Austria |
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| Subject | data mining; functional genomics; disease modeling; bioinformatics |
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| Abstract | Analyzing 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. |
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| Publication date | 2003 |
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| In | |
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| Language | English |
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| NRC number | NRCC 45789 |
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| NPARC number | 8913255 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | e4c8b611-9770-4f2f-a23d-0ba5b3e54bca |
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| Record created | 2009-04-22 |
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| Record modified | 2021-01-05 |
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