Laplacian Matrix Based Spectral Graph Clustering
Ambika P. R.1, Bharathi Malakreddy A.2

1Ambika P. R., Assistant Professor, Department of CSE, City Engineering College, Bengaluru (Karnataka), India. 

2Dr. Bharathi Malkreddy A., Professor and HOD, Department of Artificial Intelligence and Machine Learning, Visvesvaraya Technological University, BMS Institute of Technology and Management, Bangalore (Karnataka), India. 

Manuscript received on 08 December 2019 | Revised Manuscript received on 16 December 2019 | Manuscript Published on 31 December 2019 | PP: 666-670 | Volume-9 Issue-2S December 2019 | Retrieval Number: B11081292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1108.1292S19

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Abstract: Recent attention in the research field of clustering is focused on grouping of clusters based on structure of a graph. At present, there are plentiful literature work has been proposed towards the clustering techniques but it is still an open challenge to find the best technique for clustering. This paper present a comprehensive review of our insights towards emerging clustering methods on graph based spectral clustering. Graph Laplacians have become a core technology for the spectral clustering which works based on the properties of the Laplacian matrix. In our study, we discuss correlation between similarity and Laplacian matrices within a graph and spectral graph theory concepts. Current studies on graph-based clustering methods requires a well defined good quality graph to achieve high clustering accuracy. This paper describes how spectral graph theory has been used in the literature of clustering concepts and how it helps to predict relationships that have not yet been identified in the existing literature. Some application areas on the graph clustering algorithms are discussed. This survey outlines the problems addressed by the existing research works on spectral clustering with its problems, methodologies, data sets and advantages. This paper identifies fundamental issues of graph clustering which provides a better direction for more applications in social network analysis, image segmentation, computer vision and other domains.

Keywords: Clustering, Laplacian, Spectral Graph.
Scope of the Article: Clustering