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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Detection of Outlier-Communities using Minimum Spanning Tree

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Author S. Chidambaranathan, S. John Peter
ISSN 2079-8407
On Pages 608-614
Volume No. 2
Issue No. 11
Issue Date November 01, 2011
Publishing Date November 01, 2011
Keywords Euclidean minimum spanning tree, Clustering, Eccentricity, Center, Community validity, Community Separation, Outliers


Abstract

Community (also known as clusters) is a group of nodes with dense connection. Detecting outlier-communities from database is a big desire. In this paper we propose a novel Minimum Spanning Tree based algorithm for detecting outlier-communities from complex networks. The algorithm uses a new community validation criterion based on the geometric property of data partition of the data set in order to find the proper number of communities. The algorithm works in two phases. The first phase of the algorithm creates optimal number of communities, whereas the second phase of the algorithm finds outlier-communities.

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