Research Article
Article:A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 3 - Issue 6 |
| Published: June 2010 |
| Authors: Anant Ram, Sunita Jalal, Anand S. Jalal, Manoj Kumar |
10.5120/739-1038
|
Anant Ram, Sunita Jalal, Anand S. Jalal, Manoj Kumar . Article:A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases. International Journal of Computer Applications. 3, 6 (June 2010), 1-4. DOI=10.5120/739-1038
@article{ 10.5120/739-1038,
author = { Anant Ram,Sunita Jalal,Anand S. Jalal,Manoj Kumar },
title = { Article:A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases },
journal = { International Journal of Computer Applications },
year = { 2010 },
volume = { 3 },
number = { 6 },
pages = { 1-4 },
doi = { 10.5120/739-1038 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2010
%A Anant Ram
%A Sunita Jalal
%A Anand S. Jalal
%A Manoj Kumar
%T Article:A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases%T
%J International Journal of Computer Applications
%V 3
%N 6
%P 1-4
%R 10.5120/739-1038
%I Foundation of Computer Science (FCS), NY, USA
Abstract
DBSCAN is a base algorithm for density based clustering. It can detect the clusters of different shapes and sizes from the large amount of data which contains noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. In this paper, we propose a density varied DBSCAN algorithm which is capable to handle local density variation within the cluster. It calculates the growing cluster density mean and then the cluster density variance for any core object, which is supposed to be expended further, by considering density of its -neighborhood with respect to cluster density mean. If cluster density variance for a core object is less than or equal to a threshold value and also satisfying the cluster similarity index, then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results.
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