Fuzzy Shared Nearest Neighbor Clustering

Abstract

Shared nearest neighbor (SNN) clustering algorithm is a robust graph-based, efficient clustering method that could handle high-dimensional data. The SNN clustering works well when the data consist of clusters that are of diverse in shapes, densities, and sizes but assignment of the data points lying in the boundary regions of overlapping clusters is not accurate. In order to overcome this problem, we have presented an extension of shared nearest neighbor algorithm that have better capability of handling the data points lying in the boundary regions specifically for overlapping cluster by means of fuzzy concept. Extensive experiments were carried out to compare the proposed approach fuzzy shared nearest neighbor clustering (FSNN) with existing clustering methods K-means, Fuzzy C-means, Density_clust, and Shared Nearest Neighbor. The effectiveness of FSNN is evaluated in benchmark datasets. Experimental results using FSNN method show that it can accurately cluster the data points lying in the overlapping partition and generate compact and well-separated clusters as compared to state-of-the-art clustering algorithm. The results obtained using different clustering methods are validated by standard cluster validation measures.