On Feature Selection through Clustering

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We study an algorithm for feature selection that clusters attributes using a special metric and then makes use of the dendrogram of the resulting cluster hierarchy to choose the most relevant attributes. The main interest of our technique resides in the improved understanding of the structure of the analyzed data and of the relative importance of the attributes for the selection process.

Richard Butterworth, Gregory Piatetsky-Shapiro, Da

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Cluster Hierarchy | Data Mining | Feature Selection | ICDM 2005 | Relevant Attributes |

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Added	24 Jun 2010
Updated	24 Jun 2010
Type	Conference
Year	2005
Where	ICDM
Authors	Richard Butterworth, Gregory Piatetsky-Shapiro, Dan A. Simovici

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On Feature Selection through Clustering

Cluster Hierarchy | Data Mining | Feature Selection | ICDM 2005 | Relevant Attributes |

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