CLUES: A Nonparametric Clustering Method Based on Local Shrinking

• Activity Number: 236
• Type: Topic Contributed
• Date/Time: Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Xiaogang Wang*+
• Companies: York University
• Keywords: Clustering ; Local Shrinking ; K-nearest neighbor ; Number of clusters ;

Abstract

The authors propose a novel non-parametric clustering method based on non-parametric local shrinking. Each data point is transformed in such a way that it moves a specific distance toward a cluster center. The direction and the associated size of each movement are determined by the median of its K-nearest neighbors. The optimal value of the number of neighbors is determined by optimizing some commonly used index functions that measure the strengths of clusters generated by the algorithm. The number of clusters and the final partition are determined automatically without any input parameter except the stopping rule for convergence. The experiments on simulated and real datasets suggest that the proposed algorithm achieves relatively high accuracies when compared with classical clustering algorithms.