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Choosing DBSCAN Parameters Automatically using Differential Evolution

by Amin Karami, Ronnie Johansson
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 7
Year of Publication: 2014
Authors: Amin Karami, Ronnie Johansson
10.5120/15890-5059

Amin Karami, Ronnie Johansson . Choosing DBSCAN Parameters Automatically using Differential Evolution. International Journal of Computer Applications. 91, 7 ( April 2014), 1-11. DOI=10.5120/15890-5059

@article{ 10.5120/15890-5059,
author = { Amin Karami, Ronnie Johansson },
title = { Choosing DBSCAN Parameters Automatically using Differential Evolution },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 7 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number7/15890-5059/ },
doi = { 10.5120/15890-5059 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:06.915796+05:30
%A Amin Karami
%A Ronnie Johansson
%T Choosing DBSCAN Parameters Automatically using Differential Evolution
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 7
%P 1-11
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster) which both have a significant influence on the clustering results. Hence, DBSCAN is sensitive to its input parameters and it is hard to determine them a priori. This paper presents an efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts. Since the Eps parameter can largely degrades the efficiency of the DBSCAN algorithm, the combination of an analytical way for estimating Eps and Tournament Selection (TS) method is also employed. Experimental results indicate the proposed method is precise in determining appropriate input parameters of DBSCAN algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

Clustering Analysis DBSCAN Differential Evolution Tournament Selection

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