A novel pruning approach for robust data clustering

In this paper, we make an effort to overcome the sensitivity of traditional clustering algorithms to noisy data points (noise and outliers). A novel pruning method, in terms of information theory, is therefore proposed to phase out noisy points for robust data clustering. This approach identifies and prunes the noisy points based on the maximization of mutual information against input data distributions such that the resulting clusters are least affected by noise and outliers, where the degree of robustness is controlled through a separate parameter to make a trade-off between rejection of noisy points and optimal clustered data. The pruning approach is general, and it can improve the robustness of many existing traditional clustering methods. In particular, we apply the pruning approach to improve the robustness of fuzzy c-means clustering and its extensions, e.g., fuzzy c-spherical shells clustering and kernel-based fuzzy c-means clustering. As a result, we obtain three clustering algorithms that are the robust versions of the existing ones. The effectiveness of the proposed pruning approach is supported by experimental results.