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Considering the close relation between k-Means clustering and principal component analysis (PCA), a cluster validation approach for k-Means partitions was proposed using analytical solutions of PCA. In this paper, the validation approach is further extended for handling mixed databases composed of not only numerical observations but also categorical observations. In the new validation approach for...
This paper considers k-Means clustering of incomplete data sets including missing values. Although the main purpose of k-Means clustering is to partition samples into several homogeneous clusters by minimizing within-cluster errors, it has been shown that a relaxed solution of k-Means can be recovered in a PCA-guided manner. In this paper, the PCA-guided k-Means procedure is extended to a situation...
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