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Concept learning and feature selection based on square-error clustering
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DOI：10.1023/A:1007567018844.png)
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摘要
En 中文
Based on a reinterpretation of the square-error criterion for classical clustering, a separate-and-conquer version of K-Means clustering is presented and a contribution weight is determined for each variable of every cluster. The weight is used to produce conjunctive concepts that describe clusters and to reduce or transform the variable (feature) space.
Keyword:
clustering
variable weights
conjunctive concepts
feature selection
feature space transformation
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