An improved fuzzy k-means clustering with k-center initialization

Abstract

Initialization of fuzzy k-means algorithm decreases the convergent rate of clustering and leads to plenty of calculation. Thus, we propose an improved fuzzy k-means clustering based on k-center algorithm and binary tree in this paper, which firstly reduces redundant attributes while too many irrespective attributes affect the efficiency of clustering. Secondly, we remove the differences of units of dimensions, and then adopt k-center clustering to initialize k means of clusters, which means that we choose first mean randomly and others obtained according to distance subsequently. The binary tree is composed of k means in order to find its closest mean easily. Finally, the proposed algorithm is applied on Iris dataset, Pima-Indians-Diabetes dataset and Segmentation dataset, and results show that the proposed algorithm has higher efficiency and greater precision, and reduces the amount of calculation.