An Extended DBSCAN Clustering Algorithm

Finding clusters of different densities is a challenging task. DBSCAN “Density-Based Spatial Clustering of Applications with Noise” method has trouble discovering clusters of various densities since it uses a fixed radius. This article proposes an extended DBSCAN for finding clusters of different de...

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Veröffentlicht in:International journal of advanced computer science & applications Jg. 13; H. 3
1. Verfasser: Fahim, Ahmed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: West Yorkshire Science and Information (SAI) Organization Limited 2022
Schlagworte:
Algorithms
Cluster analysis
Clustering
Computer science
Datasets
Density
Neighborhoods
Noise
Similarity
ISSN:2158-107X, 2156-5570
Online-Zugang:Volltext
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Zusammenfassung:Finding clusters of different densities is a challenging task. DBSCAN “Density-Based Spatial Clustering of Applications with Noise” method has trouble discovering clusters of various densities since it uses a fixed radius. This article proposes an extended DBSCAN for finding clusters of different densities. The proposed method uses a dynamic radius and assigns a regional density value for each object, then counts the objects of similar density within the radius. If the neighborhood size ≥ MinPts, then the object is a core, and a cluster can grow from it, otherwise, the object is assigned noise temporarily. Two objects are similar in local density if their similarity ≥ threshold. The proposed method can discover clusters of any density from the data effectively. The method requires three parameters; MinPts, Eps (distance to the kth neighbor), and similarity threshold. The practical results show the superior ability of the suggested method to detect clusters of different densities even with no discernible separations between them.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2022.0130331