A NEW SPECTRAL CLUSTERING APPROACH TO DETECTING COMMUNITIES IN GRAPHS
Keywords:
Clustering, Spectral Clustering, Similarity Matrix, Graph Laplacians, Language RAbstract
Recently, clustering is one of the most important approaches used for exploratory data analysis. It is one of the most widely used approaches for exploratory data analysis. Spectral Clustering (SC) is a technique which relies on the eigenstructure of a similarity matrix to partition points into disjoint clusters with the points in the same cluster having high similarity and the points in various clusters having low similarity.
Clustering nodes in graph is a general technique used in data mining for large network data sets. This paper presents an approach for the detection of communities from graphs with Spectral Clustering. In this research paper, for simulation and implementation, we used igraph package in language R.
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