Hybrid k-Means: Combining Regression-Wise and Centroid-Based Criteria for QSAR
This paper further extends the ‘kernel’-based approach to clustering proposed by E. Diday in early 70s. According to this approach, a cluster’s centroid can be represented by parameters of any analytical model, such as linear regression equation, built over the cluster. We address the problem of producing regression-wise clusters to be separated in the input variable space by building a hybrid clustering criterion that combines the regression-wise clustering criterion with the conventional centroid-based one.
KeywordsFeature Space Loss Function Hybrid Model Cluster Criterion Relative Prediction Error
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