A novel similarity measure for data clustering

Article type: Research Article

Authors: Yao, Yuhui | Chen, Yan Qiu | Chen, Lihui

Affiliations: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798. E-mail: [email protected]

Abstract: A novel similarity measure, proposed for clustering data with arbitrary distribution shapes, is developed. Such a new measure of similarity is employed in a dynamic model to collectively measure similarity among pattern vectors, which can help to achieve a more robust clustering performance than using the existing measures that are staticly and individually based on the distances among the isolated pairwise data. The experiment results demonstrated that the proposed neural network based on the new similarity measure has the capability to robustly and quickly cluster data on which Cluster-Detection-and-Labeling neural network fails.

Keywords: unsupervised learning, clustering, association clustering

DOI: 10.3233/IDA-2000-4504

Journal: Intelligent Data Analysis, vol. 4, no. 5, pp. 421-431, 2000