K-Neighbourhood Structural Similarity Approach for Spatial Clustering

Abstract

Background/Objectives: The scope and the diversity of contexts in which graphs are used in the field of network analysis have created scope for interdisciplinary research. It has proved to be an efficient structure to understand the features, the topological structure and the dynamics of many complex systems. Methods/Statistical Analysis: This paper proposes a graph clustering method on a directed weighted graph network for detecting communities in social network, based on neighbourhood nodes and the frequency of the path traversed. The algorithm is evaluated with small, medium and large sized data. The algorithm is experimented with the data and its performance is evaluated in terms of cluster quality. Findings: Our results represent a novel method for identifying quality clusters with high intra-similarity and low intersimilarity. The significance of the algorithm is it automatically generates the parameters needed for clustering and also the number of clusters to be formed from the dataset. Experimental results obtained on the mobility and citation dataset prove the efficiency of k-Neighbourhood Structural Similarity algorithm. Application/Improvements: Clustering, a widely used data mining technique finds its application in areas of healthcare, banking, finance and decision making. This algorithm considers the spatial aspect and as a future enhancement we would analyze the influence of temporal factor in forming good clusters.