CLUES: A non-parametric clustering method based on local shrinking

In this paper, we 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. This process is repeated until a pre-defined convergence criterion is satisfied. 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. Our experiments on simulated and real data sets suggest that that the proposed algorithm achieves relatively high accuracies when compared with classical clustering algorithms.

• Type: Journal
• Year: 2007
• Where: CSDA
• Authors: Xiaogang Wang, Weiliang Qiu, Ruben H. Zamar
• Keywords: CSDA 2007, K-nearest Neighbors, Non-parametric Local Shrinking, Pre-defined Convergence Criterion