| Download | - View accepted manuscript: Relevant Attribute Discovery in High Dimensional Data: Application to Breast Cancer Gene Expressions (PDF, 281 KiB)
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| Author | Search for: Valdés, Julio; Search for: Barton, Alan |
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| Format | Text, Article |
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| Conference | First International Conference on Rough Sets and Knowledge Technology (RSKT 2006), July 24-26, 2006 |
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| Abstract | In many domains, the data objects are described in terms of a large number of features. The pipelined data mining approach introduced in [12] using two clustering algorithms in combination with rough sets and extended with genetic programming, is investigated with the purpose of discovering important subsets of attributes in high dimensional data. Their classification ability is described in terms of both collections of rules and analytic functions obtained by genetic programming (gene expression programming). The Leader and several k-means algorithms are used as procedures for attribute set simplification of the information systems later presented to rough sets algorithms. Visual data mining techniques including virtual reality were used for inspecting results. The data mining process is setup using high throughput distributed computing techniques. This approach was applied to Breast Cancer gene expression data and it led to subsets of genes with high discrimination power with respect to the decision classes. |
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| Publication date | 2006 |
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| In | |
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| Language | English |
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| NRC number | NRCC 48721 |
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| NPARC number | 8913666 |
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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 | 951fba9e-760b-4970-95b5-e82fc7497ad6 |
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| Record created | 2009-04-22 |
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| Record modified | 2020-10-09 |
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