An improved DBSCAN algorithm based on cell-like P systems with promoters and inhibitors
Density-based spatial clustering of applications with noise (DBSCAN) algorithm can find clusters of arbitrary shape, while the noise points can be removed. Membrane computing is a novel research branch of bio-inspired computing, which seeks to discover new computational models/framework from biologi...
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| Published in | PloS one Vol. 13; no. 12; p. e0200751 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
17.12.2018
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0200751 |
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| Abstract | Density-based spatial clustering of applications with noise (DBSCAN) algorithm can find clusters of arbitrary shape, while the noise points can be removed. Membrane computing is a novel research branch of bio-inspired computing, which seeks to discover new computational models/framework from biological cells. The obtained parallel and distributed computing models are usually called P systems. In this work, DBSCAN algorithm is improved by using parallel evolution mechanism and hierarchical membrane structure in cell-like P systems with promoters and inhibitors, where promoters and inhibitors are utilized to regulate parallelism of objects evolution. Experiment results show that the proposed algorithm performs well in big cluster analysis. The time complexity is improved to O(n), in comparison with conventional DBSCAN of O(n2). The results give some hints to improve conventional algorithms by using the hierarchical framework and parallel evolution mechanism in membrane computing models. |
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| AbstractList | Density-based spatial clustering of applications with noise (DBSCAN) algorithm can find clusters of arbitrary shape, while the noise points can be removed. Membrane computing is a novel research branch of bio-inspired computing, which seeks to discover new computational models/framework from biological cells. The obtained parallel and distributed computing models are usually called P systems. In this work, DBSCAN algorithm is improved by using parallel evolution mechanism and hierarchical membrane structure in cell-like P systems with promoters and inhibitors, where promoters and inhibitors are utilized to regulate parallelism of objects evolution. Experiment results show that the proposed algorithm performs well in big cluster analysis. The time complexity is improved to O(n), in comparison with conventional DBSCAN of O(n2). The results give some hints to improve conventional algorithms by using the hierarchical framework and parallel evolution mechanism in membrane computing models. Density-based spatial clustering of applications with noise (DBSCAN) algorithm can find clusters of arbitrary shape, while the noise points can be removed. Membrane computing is a novel research branch of bio-inspired computing, which seeks to discover new computational models/framework from biological cells. The obtained parallel and distributed computing models are usually called P systems. In this work, DBSCAN algorithm is improved by using parallel evolution mechanism and hierarchical membrane structure in cell-like P systems with promoters and inhibitors, where promoters and inhibitors are utilized to regulate parallelism of objects evolution. Experiment results show that the proposed algorithm performs well in big cluster analysis. The time complexity is improved to O(n), in comparison with conventional DBSCAN of O(n.sup.2). The results give some hints to improve conventional algorithms by using the hierarchical framework and parallel evolution mechanism in membrane computing models. Density-based spatial clustering of applications with noise (DBSCAN) algorithm can find clusters of arbitrary shape, while the noise points can be removed. Membrane computing is a novel research branch of bio-inspired computing, which seeks to discover new computational models/framework from biological cells. The obtained parallel and distributed computing models are usually called P systems. In this work, DBSCAN algorithm is improved by using parallel evolution mechanism and hierarchical membrane structure in cell-like P systems with promoters and inhibitors, where promoters and inhibitors are utilized to regulate parallelism of objects evolution. Experiment results show that the proposed algorithm performs well in big cluster analysis. The time complexity is improved to O(n), in comparison with conventional DBSCAN of O(n2). The results give some hints to improve conventional algorithms by using the hierarchical framework and parallel evolution mechanism in membrane computing models.Density-based spatial clustering of applications with noise (DBSCAN) algorithm can find clusters of arbitrary shape, while the noise points can be removed. Membrane computing is a novel research branch of bio-inspired computing, which seeks to discover new computational models/framework from biological cells. The obtained parallel and distributed computing models are usually called P systems. In this work, DBSCAN algorithm is improved by using parallel evolution mechanism and hierarchical membrane structure in cell-like P systems with promoters and inhibitors, where promoters and inhibitors are utilized to regulate parallelism of objects evolution. Experiment results show that the proposed algorithm performs well in big cluster analysis. The time complexity is improved to O(n), in comparison with conventional DBSCAN of O(n2). The results give some hints to improve conventional algorithms by using the hierarchical framework and parallel evolution mechanism in membrane computing models. |
| Audience | Academic |
| Author | Li, Xiufeng Liu, Xiyu Zhao, Yuzhen |
| AuthorAffiliation | College of Business, Shandong Normal University, Jinan, 250014, China Tokai University, JAPAN |
| AuthorAffiliation_xml | – name: College of Business, Shandong Normal University, Jinan, 250014, China – name: Tokai University, JAPAN |
| Author_xml | – sequence: 1 givenname: Yuzhen orcidid: 0000-0003-4902-1120 surname: Zhao fullname: Zhao, Yuzhen – sequence: 2 givenname: Xiyu surname: Liu fullname: Liu, Xiyu – sequence: 3 givenname: Xiufeng surname: Li fullname: Li, Xiufeng |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30557333$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_molp_2020_12_012 crossref_primary_10_1016_j_neunet_2023_08_013 crossref_primary_10_1186_s13321_023_00741_9 crossref_primary_10_1016_j_xops_2022_100166 crossref_primary_10_3390_math11184008 crossref_primary_10_3390_e24060834 crossref_primary_10_1016_j_ic_2021_104786 crossref_primary_10_1016_j_matpr_2021_06_441 |
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| SubjectTerms | Algorithms Artificial intelligence Biology and Life Sciences Biomimetics Cluster analysis Clustering Computation Computer and Information Sciences Computer applications Computer networks Computer simulation Cytology Distributed processing Engineering and Technology Evolution Evolution, Molecular Evolutionary algorithms Genetic algorithms Information science Inhibitors Mathematical models Membrane structure Membrane structures Models, Genetic Neighborhoods Neural networks Noise Physical Sciences Promoter Regions, Genetic Promoters Research and Analysis Methods Science Sequence Analysis, DNA - methods Structural hierarchy |
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| Title | An improved DBSCAN algorithm based on cell-like P systems with promoters and inhibitors |
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