Modified DBSCAN Clustering Algorithm for Data with Different Densities.

Dawoud, Hassan and Ashour, Wesam (2012) Modified DBSCAN Clustering Algorithm for Data with Different Densities. COMPUTING AND INFORMATION SYSTEMS JOURNAL, 16 (3). pp. 16-21. ISSN 1461-6122

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Abstract: The problem of detecting clusters of points in data is challenging when the clusters are of different size, density and shape. The density based clustering algorithm DBSCAN is one of the most popular density based algorithms. The DBSCAN algorithm has a limitation when dealing with data of different densities. In this paper we propose an algorithm based on the DBSCAN. The proposed algorithm is capable of clustering data with arbitrary shapes and dealing with different densities of data. The Idea of the proposed algorithm is to update the eps and MinPts (where eps and MinPts are input parameters of DBSCAN algorithm) values according to the densities of regions of data points. These values are scaled depending on eps-neighborhood points. In the experiments we apply the proposed algorithm to artificial dataset and real dataset as we will show in the last section of the paper.

Item Type: Article
Subjects: T Technology > T Technology (General)
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: م. حسن محمد حسن داود
Date Deposited: 11 Mar 2018 09:05
Last Modified: 11 Mar 2018 09:05

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