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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 295067, 9 pages
Research Article

Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data

1School of Mathematics, Hefei University of Technology, Hefei 230009, China
2Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
3Department of Automation, University of Science and Technology of China, Hefei 230027, China

Received 8 October 2012; Revised 20 May 2013; Accepted 20 May 2013

Academic Editor: Jun Zhao

Copyright © 2013 JingDong Tan and RuJing Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Sharing nearest neighbor (SNN) is a novel metric measure of similarity, and it can conquer two hardships: the low similarities between samples and the different densities of classes. At present, there are two popular SNN similarity based clustering methods: JP clustering and SNN density based clustering. Their clustering results highly rely on the weighting value of the single edge, and thus they are very vulnerable. Motivated by the idea of smooth splicing in computing geometry, the authors design a novel SNN similarity based clustering algorithm within the structure of graph theory. Since it inherits complementary intensity-smoothness principle, its generalizing ability surpasses those of the previously mentioned two methods. The experiments on text datasets show its effectiveness.