Welcome to the InfoSci Platform
Clustering Vertices in Weighted Graphs

Clustering Vertices in Weighted Graphs

Derry Tanti Wijaya, Stephane Bressan
Copyright: © 2012 |Pages: 14
ISBN13: 9781613500538|ISBN10: 161350053X|EISBN13: 9781613500545
DOI: 10.4018/978-1-61350-053-8.ch012
Cite Chapter Cite Chapter

MLA

Wijaya, Derry Tanti, and Stephane Bressan. "Clustering Vertices in Weighted Graphs." Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, IGI Global, 2012, pp. 285-298. https://doi.org/10.4018/978-1-61350-053-8.ch012

APA

Wijaya, D. T. & Bressan, S. (2012). Clustering Vertices in Weighted Graphs. In S. Sakr & E. Pardede (Eds.), Graph Data Management: Techniques and Applications (pp. 285-298). IGI Global. https://doi.org/10.4018/978-1-61350-053-8.ch012

Chicago

Wijaya, Derry Tanti, and Stephane Bressan. "Clustering Vertices in Weighted Graphs." In Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, 285-298. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-053-8.ch012

Export Reference

Mendeley
Favorite

Abstract

Clustering is the unsupervised process of discovering natural clusters so that objects within the same cluster are similar and objects from different clusters are dissimilar. In clustering, if similarity relations between objects are represented as a simple, weighted graph where objects are vertices and similarities between objects are weights of edges; clustering reduces to the problem of graph clustering. A natural notion of graph clustering is the separation of sparsely connected dense sub graphs from each other based on the notion of intra-cluster density vs. inter-cluster sparseness. In this chapter, we overview existing graph algorithms for clustering vertices in weighted graphs: Minimum Spanning Tree (MST) clustering, Markov clustering, and Star clustering. This includes the variants of Star clustering, MST clustering and Ricochet.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.