Soft Subspace Clustering with Competitive Agglomeration

Abstract—In this paper, two novel soft subspace clustering algorithms, namely fuzzy weighting subspace clustering with competitive agglomeration (FWSCA) and entropy weighting subspace clustering with competitive agglomeration (EWSCA), are proposed to overcome the problems of the unknown number of clusters and the initialization of prototypes for soft subspace clustering. The main advantage of FWSCA and EWSCA lies in the fact that they effectively integrate the merits of soft subspace clustering and the good properties of fuzzy clustering with competitive agglomeration. This makes it possible to obtain the appropriate number of clusters during the clustering progress. Moreover, FWSCA and EWSCA algorithms can converge regardless of the initial number of clusters and initialization. Substantial experimental results on both synthetic and real data sets demonstrate the effectiveness of FWSCA and EWSCA in addressing the two problems. Keywords-subspace clustering; competitive agglomeration; fuzzy weighting subspace clustering; entropy weighting subspace clustering; optimal number of clusters I