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Reseach Article

Local Density-based Hierarchical Clustering for Overlapping Distribution using Minimum Spanning Tree

by S. John Peter
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 12
Year of Publication: 2012
Authors: S. John Peter
10.5120/6153-8542

S. John Peter . Local Density-based Hierarchical Clustering for Overlapping Distribution using Minimum Spanning Tree. International Journal of Computer Applications. 43, 12 ( April 2012), 7-11. DOI=10.5120/6153-8542

@article{ 10.5120/6153-8542,
author = { S. John Peter },
title = { Local Density-based Hierarchical Clustering for Overlapping Distribution using Minimum Spanning Tree },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 12 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number12/6153-8542/ },
doi = { 10.5120/6153-8542 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:33:11.567002+05:30
%A S. John Peter
%T Local Density-based Hierarchical Clustering for Overlapping Distribution using Minimum Spanning Tree
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 12
%P 7-11
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we propose a clustering algorithm to find clusters of different sizes, shapes and densities. Density and Hierarchical based approaches are adopted in the algorithm using Minimum Spanning Tree, resulting in a new algorithm – Local Density-based Hierarchical Clustering Algorithm for overlapping data distribution using Minimum Spanning Tree (LDHCODMST). The algorithm is divided into two stages. In the first stage, local density is estimated at each data point. In the second stage, hierarchical approach is used by merging clusters according to the computed cluster distance based on overlap in distribution of data points. The proposed algorithm improves the effectiveness of clustering result in which data are distributed in different shapes and different density, and that it can get a better clustering efficiency.

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Index Terms

Computer Science
Information Sciences

Keywords

Euclidean Minimum Spanning Tree Clustering Core Cluster Merge Similarity