Neighborhood Density based Clustering with Agglomerative Fuzzy K-Means Algorithm

Abstract

Clustering is one of the primary tools in unsupervised
learning. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging to different groups are dissimilar. K-means is one of the most widely used algorithms in clustering because of its simplicity and performance. The initial centriod for k-means clustering is generated randomly. In this paper, we address a
method for effectively selecting initial cluster center. This method identifies the high density neighborhood (NSS) from the data and then select initial centroid of the neighborhoods as initial centers. Agglomerative Fuzzy k-means (Ak-means) clustering algorithm is then utilized to further merge these initial centers to get the preferred number of clusters and create better clustering results. Merging method is employed to produce more consistent clustering results from
different sets of initial clusters centers. Experimental observations on several data sets have proved that the proposed clustering approach was very significant in automatically identifying the true cluster
number and also providing correct clustering results.