A Novel Clustering Algorithm Based on Variable Precision Rough-Fuzzy Sets

Abstract

In the field of cluster analysis and data mining, fuzzy c-means algorithm is one of effective methods, which has widely used in unsupervised pattern classification. However, the above algorithm assumes that each feature of the samples plays a uniform contribution for cluster analysis. To consider the different contribution of each dimensional feature of the given samples to be classified, this paper presents a novel fuzzy c-means clustering algorithm based on feature weighted, in which the Variable Precision Rough-Fuzzy Sets is used to assign the weights to each feature. Due to the advantages of Rough Sets for feature reduction, we can obtain the better results than the traditional one, which enriches the theory of FCM-type algorithms. Then, we apply the proposed method into video data to detect shot boundary in video indexing and browsing. The test experiment with UCI data and the video data from CCTV demonstrate the effectiveness of the novel algorithm.

This work was supported by National Natural Science Foundation of China (No. 60102005) and National Key Lab. Foundation (J14203220033).