Robust Fuzzy Clustering Based on Similarity between Data
Kohei Inoue, and Kiichi Urahama
Faculty of Design, Kyushu University, 4-9-1 Shiobaru, Minami-ku, Fukuoka-shi 815-8540, Japan
Received:June 26, 2003Accepted:December 1, 2003Published:March 20, 2004
Keywords:robust fuzzy clustering, relational data, asymmetric similarity, arbitrarily shaped cluster
We present a robust fuzzy clustering method that utilizes a sequential cluster extraction scheme. In contrast to heuristic sequential methods, our algorithm is derived from an optimization problem and is an iterative solution to it. Our method is non-parametric and includes no heuristic parameter, and can deal with asymmetric similarity data. The determination of the number of clusters is simple and is based on a monotonic property of extracted cluster volumes. Our method can extract arbitrarily shaped clusters by extending the measure of distance between data to a shortest path length. The performance of the method is demonstrated for clustering of an image database and the segmentation of images.
Cite this article as:K. Inoue and K. Urahama, “Robust Fuzzy Clustering Based on Similarity between Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.8 No.2, pp. 115-120, 2004.Data files: