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Reseach Article

Cluster based Outlier Detection

by Pranjali Kasture, Jayant Gadge
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 10
Year of Publication: 2012
Authors: Pranjali Kasture, Jayant Gadge
10.5120/9317-3549

Pranjali Kasture, Jayant Gadge . Cluster based Outlier Detection. International Journal of Computer Applications. 58, 10 ( November 2012), 11-15. DOI=10.5120/9317-3549

@article{ 10.5120/9317-3549,
author = { Pranjali Kasture, Jayant Gadge },
title = { Cluster based Outlier Detection },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 10 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number10/9317-3549/ },
doi = { 10.5120/9317-3549 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:05.133151+05:30
%A Pranjali Kasture
%A Jayant Gadge
%T Cluster based Outlier Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 10
%P 11-15
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Outlier detection is a fundamental issue in data mining, specifically it has been used to detect and remove anomalous objects from data. mining. The proposed approach to detect outlier includes three methods which are clustering, pruning and computing outlier score. For clustering k-means algorithm is used which partition the dataset into given number of clusters. In pruning, based on some distance measure, points which are closed to centroid of each cluster are pruned. For the unpruned points, local distance based outlier factor (LDOF) measure is calculated. A measure called LDOF, tells how much a point is deviating from its neighbors. The high LDOF value of a point indicates that the point is deviating more from its neighbors and probably it may be an outlier.

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Index Terms

Computer Science
Information Sciences

Keywords

Outlier cluster pruning outlier score k nearest neighbor