Generalized Dirichlet-Process-Means for Robust and Maximum Distortion Criteria
Masahiro Kobayashi, Kazuho Watanabe · 2018
DP-means clustering was obtained as an extension of K-means clustering. While it is implemented with a simple and efficient algorithm, it can estimate the number of clusters simultaneously. However, DP-means is specifically designed for the average distortion criterion. Therefore, it is vulnerable to outliers in data, and can cause large maximum distortion in clusters. This study introduces a new parameter to the objective function of DP-means to provide an extension of DP-means, which bridges robust estimation of cluster centers and minimization of the maximum distortion criterion.