  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 measure. Therefore, it is vulnerable to
outliers in data, and can cause large maximum distortion in clusters. In this
work, we extend the objective function of the DP-means to $f$-separable
distortion measures and propose a unified learning algorithm to overcome the
above problems by selecting the function $f$. Further, the influence function
of the estimated cluster center is analyzed to evaluate the robustness against
outliers. We demonstrate the performance of the generalized method by numerical
experiments using real datasets.
