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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Vydáno v:Mathematical problems in engineering Ročník 2018; číslo 2018; s. 1 - 16
Hlavní autoři: Jun, Li, Dongyong, Yang, Jinyin, Chen, Jungan, Chen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Témata:
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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Abstract 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.
AbstractList 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.
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.
Author Jun, Li
Dongyong, Yang
Jungan, Chen
Jinyin, Chen
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CitedBy_id crossref_primary_10_1016_j_jocs_2022_101925
crossref_primary_10_32604_cmc_2023_036820
crossref_primary_10_1007_s12652_020_01741_5
crossref_primary_10_1080_19427867_2020_1733199
Cites_doi 10.1109/TIP.2016.2559803
10.1007/978-3-642-13818-8_34
10.1126/science.1242072
10.1109/TIP.2009.2025923
10.1016/j.patrec.2011.02.001
10.1007/978-3-642-23878-9_53
ContentType Journal Article
Copyright Copyright © 2018 Chen Jungan et al.
Copyright © 2018 Chen Jungan et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Snippet Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varied densities. To solve the problem, this paper extends...
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SubjectTerms Algorithms
Artificial intelligence
Bioinformatics
Clustering
Clusters
Computer science
Datasets
Density
Deviation
Engineering
International conferences
Neighborhoods
Noise
Parameter identification
Probability distribution
Similarity measures
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