Kernel-Based Methods to Identify Overlapping Clusters with Linear and Nonlinear Boundaries

Abstract

Detecting overlapping structures and identifying non-linearly-separable clusters with complex shapes are two major issues in clustering. This paper presents two kernel based methods that produce overlapping clusters with both linear and nonlinear boundaries. To improve separability of input patterns, we used for both methods Mercer kernel technique. First, we propose Kernel Overlapping K-means I (KOKMI), a centroid based method, generalizing kernel K-means to produce nondisjoint clusters with nonlinear separations. Second, we propose Kernel Overlapping K-means II (KOKMII), a medoid based method improving the previous method in terms of efficiency and complexity. Experiments performed on non-linearly-separable and real multi-labeled data sets show that proposed learning methods outperform the existing ones.