A k-Deviation Density Based Clustering Algorithm

Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varied densities. To solve the problem, this paper extends DBSCAN by exploiting a new density definition and proposes a novel algorithm called k-deviation density based DBSCAN (kDDBSCAN). Various datasets...

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Veröffentlicht in:Mathematical problems in engineering Jg. 2018; H. 2018; S. 1 - 16
Hauptverfasser: Jun, Li, Dongyong, Yang, Jinyin, Chen, Jungan, Chen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Artificial intelligence
Bioinformatics
Clustering
Clusters
Computer science
Datasets
Density
Deviation
Engineering
International conferences
Neighborhoods
Noise
Parameter identification
Probability distribution
Similarity measures
ISSN:1024-123X, 1563-5147
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Zusammenfassung:Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varied densities. To solve the problem, this paper extends DBSCAN by exploiting a new density definition and proposes a novel algorithm called k-deviation density based DBSCAN (kDDBSCAN). Various datasets containing clusters with arbitrary shapes and different or varied densities are used to demonstrate the performance and investigate the feasibility and practicality of kDDBSCAN. The results show that kDDBSCAN performs better than DBSCAN.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1024-123X
1563-5147
DOI:10.1155/2018/3742048