Detection of Outlier-Communities using Minimum Spanning Tree
|
Full Text |
Pdf |
|
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.
Back