Multiple kernel based collaborative fuzzy clustering algorithm

Cluster is found as one of the best useful tools for data analysis, data mining, and pattern recognition. The FCM algorithm and its variants algorithms has been extensively used in problems of clustering or collaborative clustering. In this paper, we present a novel method involving multiple kernel technique and FCM for collaborative clustering problem. These method endowed with multiple kernel technique which transform implicitly the feature space of input data into a higher dimensional via a non linear map, which increases greatly possibility of linear separability of the patterns when the data structure of input patterns is non-spherical and complex. To evaluate the proposed method, we use the criteria of fuzzy silhouette, a sum of squared error and classification rate to show the performance of the algorithms. © Springer-Verlag Berlin Heidelberg 2016.