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DBCLUM: Density-based Clustering and Merging Algorithm

by Mohammad Fawzy, Amr Badr, Mostafa Reda, Ibrahim Farag
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Number 14
Year of Publication: 2013
Authors: Mohammad Fawzy, Amr Badr, Mostafa Reda, Ibrahim Farag
10.5120/13806-1732

Mohammad Fawzy, Amr Badr, Mostafa Reda, Ibrahim Farag . DBCLUM: Density-based Clustering and Merging Algorithm. International Journal of Computer Applications. 79, 14 ( October 2013), 1-6. DOI=10.5120/13806-1732

@article{ 10.5120/13806-1732,
author = { Mohammad Fawzy, Amr Badr, Mostafa Reda, Ibrahim Farag },
title = { DBCLUM: Density-based Clustering and Merging Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 79 },
number = { 14 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number14/13806-1732/ },
doi = { 10.5120/13806-1732 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:52:57.377023+05:30
%A Mohammad Fawzy
%A Amr Badr
%A Mostafa Reda
%A Ibrahim Farag
%T DBCLUM: Density-based Clustering and Merging Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 14
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering is a primary method for DB mining. The clustering process becomes very challenge when the data is different densities, different sizes, different shapes, or has noise and outlier. Many existing algorithms are designed to find clusters. But, these algorithms lack to discover clusters of different shapes, densities and sizes. This paper presents a new algorithm called DBCLUM which is an extension of DBSCAN to discover clusters based on density. DBSCAN can discover clusters with arbitrary shapes. But, fail to discover different-density clusters or adjacent clusters. DBCLUM is developed to overcome these problems. DBCLUM discovers clusters individually then merges them if they are density similar and joined. By this concept, DBCLUM can discover different-densities clusters and adjacent clusters. Experiments revealed that DBCLUM is able to discover adjacent clusters and different-densities clusters and DBCLUM is faster than DBSCAN with speed up ranges from 11% to 52%.

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Index Terms

Computer Science
Information Sciences

Keywords

Data mining DBSCAN Density-Based Clustering

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