Multiple-kernel based soft subspace fuzzy clustering

Soft subspace fuzzy clustering algorithms have been successfully utilized for high dimensional data in recent studies. However, the existing works often utilize only one distance function to evaluate the similarity between data items along with each feature, which leads to performance degradation for some complex data sets. In this work, a novel soft subspace fuzzy clustering algorithm MKEWFC-K is proposed by extending the existing entropy weight soft subspace clustering algorithm with a multiple-kernel learning setting. By incorporating multiple-kernel learning strategy into the framework of soft subspace fuzzy clustering, MKEWFC-K can learning the distance function adaptively during the clustering process. Moreover, it is more immune to ineffective kernels and irrelevant features in soft subspace, which makes the choice of kernels less crucial. Experiments on real-world data demonstrate the effectiveness of the proposed MKEWFC-K algorithm.