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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| Published in: | Mathematical problems in engineering Vol. 2018; no. 2018; pp. 1 - 16 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2018
Hindawi John Wiley & Sons, Inc |
| Subjects: | |
| ISSN: | 1024-123X, 1563-5147 |
| Online Access: | Get full text |
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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. |
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| 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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| DOI | 10.1155/2018/3742048 |
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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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| Title | A k-Deviation Density Based Clustering Algorithm |
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