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Article:Outlier Removal Clustering through Minimum Spanning Tree

by T. Karthikeyan, S. John Peter
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 10
Year of Publication: 2011
Authors: T. Karthikeyan, S. John Peter

T. Karthikeyan, S. John Peter . Article:Outlier Removal Clustering through Minimum Spanning Tree. International Journal of Computer Applications. 31, 10 ( October 2011), 1-7. DOI=10.5120/3858-5382

@article{ 10.5120/3858-5382,
author = { T. Karthikeyan, S. John Peter },
title = { Article:Outlier Removal Clustering through Minimum Spanning Tree },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 10 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { },
doi = { 10.5120/3858-5382 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:17:46.950003+05:30
%A T. Karthikeyan
%A S. John Peter
%T Article:Outlier Removal Clustering through Minimum Spanning Tree
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 10
%P 1-7
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

Minimum spanning tree-based clustering algorithm is capable of detecting clusters with irregular boundaries. Detecting outliers using clustering algorithm is a big desire. Outlier detection is an extremely important task in a wide variety of application. In this paper we propose a minimum spanning tree-based clustering algorithm for detecting outliers. The algorithm partition the dataset into optimal number of clusters. Outliers are detected in the clusters based on outlyingness factor of each point (objects) in the cluster. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the data set in order to find the proper number of clusters. The algorithm works in two phases. The first phase of the algorithm creates optimal number of clusters, where as the second phase of the algorithm detect outliers.

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Index Terms

Computer Science
Information Sciences


Euclidean minimum spanning tree Eccentricity Cluster validity Cluster Separation Outlyingness factor Outliers