A survey of density based clustering algorithms

Density based clustering algorithms (DBCLAs) rely on the notion of density to identify clusters of arbitrary shapes, sizes with varying densities. Existing surveys on DBCLAs cover only a selected set of algorithms. These surveys fail to provide an extensive information about a variety of DBCLAs prop...

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Veröffentlicht in:Frontiers of Computer Science Jg. 15; H. 1; S. 151308
Hauptverfasser: BHATTACHARJEE, Panthadeep, MITRA, Pinaki
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Beijing Higher Education Press 01.02.2021
Springer Nature B.V
Schlagworte:
Algorithms
applications
Astronomy
classification
Clustering
common properties
Computer Science
Density
density based clustering
Earth sciences
Molecular biology
Multimedia
Parameter sensitivity
Review Article
survey
Taxonomy
survey
clustering
common properties
density based clustering
classification
applications
ISSN:2095-2228, 2095-2236
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Zusammenfassung:Density based clustering algorithms (DBCLAs) rely on the notion of density to identify clusters of arbitrary shapes, sizes with varying densities. Existing surveys on DBCLAs cover only a selected set of algorithms. These surveys fail to provide an extensive information about a variety of DBCLAs proposed till date including a taxonomy of the algorithms. In this paper we present a comprehensive survey of various DBCLAs over last two decades along with their classification. We group the DBCLAs in each of the four categories: density definition, parameter sensitivity, execution mode and nature of data and further divide them into various classes under each of these categories. In addition, we compare the DBCLAs through their common features and variations in citation and conceptual dependencies. We identify various application areas of DBCLAs in domains such as astronomy, earth sciences, molecular biology, geography, multimedia. Our survey also identifies probable future directions of DBCLAs where involvement of density based methods may lead to favorable results.
Bibliographie:Document received on :2019-02-17
density based clustering
survey
clustering
common properties
Document accepted on :2019-09-09
classification
applications
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2095-2228
2095-2236
DOI:10.1007/s11704-019-9059-3