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

Abstract

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.

Local contrast as an effective means to robust clustering against varying densities

“…SNN has two weaknesses. First, it is sensitive the K parameter (Brito et al 1997;Ertöz et al 2003;Tan and Wang 2013). Second, with a time complexity of O(K 2 N 2 ), it is computationally expensive.…”

“…It uses the number of shared nearest neighbours between two points as a similarity measure to replace distance in the clustering procedure. Yet, the performance of SNN is sensitive to the number of nearest neighbours used in its similarity calculations (Brito et al 1997;Ertöz et al 2003;Tan and Wang 2013). ReScale (Zhu et al 2016) is a recently proposed approach to tackle the same problem.…”