A novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm
The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhoo...
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| Vydané v: | Computer communications Ročník 174; s. 205 - 214 |
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| Jazyk: | English |
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Elsevier B.V
01.06.2021
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| ISSN: | 0140-3664, 1873-703X |
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| Abstract | The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhood (Eps) argument requires to be selected manually. For datasets with higher dimensionality and larger data volume, the selection of Eps parameters can be difficult thus leading to poor clustering quality. To solve the above problem, we propose a novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm (BSA-DBSCAN). We use the global search capability of the bird swarm method to select the best Eps parameter neighborhood values. We can avoid manual intervention and realize adaptive parameter optimization in the clustering process. To further explore the clustering performance of BSA-DBSCAN method, we test the synthetic datasets and the real-world datasets respectively and perform images analysis on the clustering evaluation index values. The simulation experiments show that the improved method in this paper can reasonably search the Eps parameter value and can obtain the higher accuracy of clustering. |
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| AbstractList | The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhood (Eps) argument requires to be selected manually. For datasets with higher dimensionality and larger data volume, the selection of Eps parameters can be difficult thus leading to poor clustering quality. To solve the above problem, we propose a novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm (BSA-DBSCAN). We use the global search capability of the bird swarm method to select the best Eps parameter neighborhood values. We can avoid manual intervention and realize adaptive parameter optimization in the clustering process. To further explore the clustering performance of BSA-DBSCAN method, we test the synthetic datasets and the real-world datasets respectively and perform images analysis on the clustering evaluation index values. The simulation experiments show that the improved method in this paper can reasonably search the Eps parameter value and can obtain the higher accuracy of clustering. |
| Author | Wang, Limin Wang, Honghuan Han, Xuming Zhou, Wei |
| Author_xml | – sequence: 1 givenname: Limin surname: Wang fullname: Wang, Limin email: wlm_new@163.com organization: School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, 510521, PR China – sequence: 2 givenname: Honghuan surname: Wang fullname: Wang, Honghuan email: whh3242337842@163.com organization: School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, PR China – sequence: 3 givenname: Xuming orcidid: 0000-0002-6213-5600 surname: Han fullname: Han, Xuming email: hanxuming@ccut.edu.cn organization: College of Information Science and Technology, Jinan University, Guangzhou 510632, PR China – sequence: 4 givenname: Wei surname: Zhou fullname: Zhou, Wei email: 2020200125@mails.cust.edu.cn organization: School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China |
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| References | Frey, Dueck (b29) 2007; 315 Li, Nguyen, Yang, Mavrovouniotis, Yang (b17) 2016; 20 Hou, Gao, Li (b19) 2016; 25 Meng, Gao, Lu, Liu, Zhang (b22) 2016; 28 Xiong, Chen, Zhang, Zhang (b26) 2012; 9 Hua, Jing, Yi, Wang, Xin (b10) 2011; 38 Chen, Zhang (b1) 2014; 275 Yaohui, Zhengming, Fang (b16) 2017; 133 Xiong, Chen, Zhang, Zhang (b12) 2012; 9 He, Tan, Luo, Mao, Ma, Feng, Fan (b8) 2011 Li, Bi, Wang, Han (b13) 2021; 167 Varo Altay, Alatas (b23) 2020; 53 Mnasri, Nasri, van den Bossche, Val (b15) 2019; 91 Hua, Jing, Yi, Wang, Xin (b20) 2011; 38 Francis, Villagrasa, Clairand (b21) 2011; 101 Borah, Bhattacharyya (b27) 2008; 3 Zhou, Wang, Li (b11) 2012; 9 Xindong, Gong-Qing, Xingquan, Wei (b2) 2014; 26 Chen, Liu, Qiu, Lai (b25) 2011; 32 Ester, Kriegel, Sander, Xu (b7) 1996 Shi, Han, Yan (b18) 2018; 104 Yang, Jiang, Shang, Norouzi (b4) 2021; 167 Rodriguez, Laio (b30) 2014; 344 Wu, Wilamowski (b3) 2016; 13 Pulabaigari, Veluru (b9) 2009; 30 Bello-Orgaz, Jung, Camacho (b5) 2016; 28 Ghasemi (b14) 2016 Peter, Antonysamy (b28) 2010; 10 Mukherjee, Goswami, Yang, Yan, Daneshmand (b6) 2020; 152 Yang, Chen, Huang (b24) 2019; 35 Wu (10.1016/j.comcom.2021.03.021_b3) 2016; 13 Zhou (10.1016/j.comcom.2021.03.021_b11) 2012; 9 Yaohui (10.1016/j.comcom.2021.03.021_b16) 2017; 133 Hua (10.1016/j.comcom.2021.03.021_b20) 2011; 38 Bello-Orgaz (10.1016/j.comcom.2021.03.021_b5) 2016; 28 Hou (10.1016/j.comcom.2021.03.021_b19) 2016; 25 Shi (10.1016/j.comcom.2021.03.021_b18) 2018; 104 Mukherjee (10.1016/j.comcom.2021.03.021_b6) 2020; 152 Meng (10.1016/j.comcom.2021.03.021_b22) 2016; 28 Pulabaigari (10.1016/j.comcom.2021.03.021_b9) 2009; 30 Peter (10.1016/j.comcom.2021.03.021_b28) 2010; 10 Francis (10.1016/j.comcom.2021.03.021_b21) 2011; 101 Ghasemi (10.1016/j.comcom.2021.03.021_b14) 2016 Xindong (10.1016/j.comcom.2021.03.021_b2) 2014; 26 Chen (10.1016/j.comcom.2021.03.021_b25) 2011; 32 Xiong (10.1016/j.comcom.2021.03.021_b26) 2012; 9 Li (10.1016/j.comcom.2021.03.021_b13) 2021; 167 Chen (10.1016/j.comcom.2021.03.021_b1) 2014; 275 Ester (10.1016/j.comcom.2021.03.021_b7) 1996 Rodriguez (10.1016/j.comcom.2021.03.021_b30) 2014; 344 Hua (10.1016/j.comcom.2021.03.021_b10) 2011; 38 Yang (10.1016/j.comcom.2021.03.021_b24) 2019; 35 Li (10.1016/j.comcom.2021.03.021_b17) 2016; 20 Mnasri (10.1016/j.comcom.2021.03.021_b15) 2019; 91 Yang (10.1016/j.comcom.2021.03.021_b4) 2021; 167 He (10.1016/j.comcom.2021.03.021_b8) 2011 Frey (10.1016/j.comcom.2021.03.021_b29) 2007; 315 Varo Altay (10.1016/j.comcom.2021.03.021_b23) 2020; 53 Borah (10.1016/j.comcom.2021.03.021_b27) 2008; 3 Xiong (10.1016/j.comcom.2021.03.021_b12) 2012; 9 |
| References_xml | – volume: 20 start-page: 590 year: 2016 end-page: 605 ident: b17 article-title: An adaptive multipopulation framework for locating and tracking multiple optima publication-title: IEEE Trans. Evol. Comput. – volume: 28 start-page: 1 year: 2016 end-page: 15 ident: b22 article-title: A new bio-inspired optimisation algorithm: Bird swarm algorithm publication-title: J. Exp. Theor. Artif. Intell. – volume: 28 start-page: 45 year: 2016 end-page: 59 ident: b5 article-title: Social big data: Recent achievements and new challenges publication-title: Inf. Fusion – volume: 315 start-page: 972 year: 2007 end-page: 976 ident: b29 article-title: Clustering by passing messages between data points publication-title: Science – volume: 35 start-page: 57 year: 2019 end-page: 65 ident: b24 article-title: An adaptive bird swarm algorithm with irregular random flight and its application publication-title: J. Comput. Sci. – volume: 275 start-page: 314 year: 2014 end-page: 347 ident: b1 article-title: Data-intensive applications, challenges, techniques and technologies: A survey on big data publication-title: Inform. Sci. – volume: 167 start-page: 75 year: 2021 end-page: 84 ident: b13 article-title: A method of two-stage clustering learning based on improved dbscan and density peak algorithm publication-title: Comput. Commun. – volume: 344 start-page: 1492 year: 2014 end-page: 1496 ident: b30 article-title: Clustering by fast search and find of density peaks publication-title: Science – start-page: 1 year: 2016 end-page: 8 ident: b14 article-title: Particle swarm optimization approach for forecasting backbreak induced by bench blasting publication-title: Neural Comput. Appl. – volume: 53 start-page: 1373 year: 2020 end-page: 1414 ident: b23 article-title: Bird swarm algorithms with chaotic mapping publication-title: Artif. Intell. Rev. – volume: 38 start-page: 9373 year: 2011 end-page: 9381 ident: b20 article-title: A new hybrid method based on partitioning-based dbscan and ant clustering publication-title: Expert Syst. Appl. – volume: 133 start-page: 208 year: 2017 end-page: 220 ident: b16 article-title: Adaptive density peak clustering based on k-nearest neighbors with aggregating strategy publication-title: Knowl.-Based Syst. – volume: 38 start-page: 9373 year: 2011 end-page: 9381 ident: b10 article-title: A new hybrid method based on partitioning-based dbscan and ant clustering publication-title: Expert Syst. Appl. – volume: 9 start-page: 1967 year: 2012 end-page: 1973 ident: b11 article-title: Research on adaptive parameters determination in dbscan algorithm publication-title: J. Inf. Comput. Sci. – volume: 10 start-page: 210 year: 2010 end-page: 214 ident: b28 article-title: Heterogeneous density based spatial clustering of application with noise publication-title: Int. J. Comput. Sci. Netw. Secur. – volume: 25 start-page: 3182 year: 2016 end-page: 3193 ident: b19 article-title: Dsets-dbscan: A parameter-free clustering algorithm publication-title: IEEE Trans. Image Process. – volume: 9 start-page: 2739 year: 2012 end-page: 2749 ident: b12 article-title: Multi-density dbscan algorithm based on density levels partitioning publication-title: J. Inf. Comput. Sci. – volume: 3 start-page: 72 year: 2008 end-page: 79 ident: b27 article-title: DDSC: A density differentiated spatial clustering technique publication-title: J. Comput. – volume: 26 start-page: 97 year: 2014 end-page: 107 ident: b2 article-title: Data mining with big data publication-title: IEEE Trans. Knowl. Data Eng. – volume: 167 start-page: 63 year: 2021 end-page: 74 ident: b4 article-title: Systematic review on next-generation web-based software architecture clustering models publication-title: Comput. Commun. – volume: 30 start-page: 1477 year: 2009 end-page: 1488 ident: b9 article-title: Rough-dbscan: A fast hybrid density based clustering method for large data sets publication-title: Pattern Recognit. Lett. – volume: 101 start-page: 265 year: 2011 end-page: 270 ident: b21 article-title: Simulation of dna damage clustering after proton irradiation using an adapted dbscan algorithm publication-title: Comput. Methods Programs Biomed. – volume: 32 start-page: 973 year: 2011 end-page: 986 ident: b25 article-title: Apscan: A parameter free algorithm for clustering publication-title: Pattern Recognit. Lett. – start-page: 473 year: 2011 end-page: 480 ident: b8 article-title: Mr-dbscan: An efficient parallel density-based clustering algorithm using mapreduce publication-title: Proceedings of the 2011 IEEE 17th International Conference on Parallel and Distributed Systems, ICPADS ’11 – volume: 152 start-page: 119 year: 2020 end-page: 125 ident: b6 article-title: Dynamic clustering method based on power demand and information volume for intelligent and green IoT publication-title: Comput. Commun. – volume: 13 start-page: 1620 year: 2016 end-page: 1628 ident: b3 article-title: A fast density and grid based clustering method for data with arbitrary shapes and noise publication-title: IEEE Trans. Ind. Inf. – volume: 9 start-page: 2739 year: 2012 end-page: 2749 ident: b26 article-title: Multi-density dbscan algorithm based on density levels partitioning publication-title: J. Inf. Comput. Sci. – volume: 91 start-page: 262 year: 2019 end-page: 280 ident: b15 article-title: A new multi-agent particle swarm algorithm based on birds accents for the 3d indoor deployment problem publication-title: ISA Trans. – start-page: 226 year: 1996 end-page: 231 ident: b7 article-title: A density-based algorithm for discovering clusters in large spatial databases with noise publication-title: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96 – volume: 104 start-page: 37 year: 2018 end-page: 44 ident: b18 article-title: Adaptive clustering algorithm based on k nn and density publication-title: Pattern Recognit. Lett. – volume: 91 start-page: 262 year: 2019 ident: 10.1016/j.comcom.2021.03.021_b15 article-title: A new multi-agent particle swarm algorithm based on birds accents for the 3d indoor deployment problem publication-title: ISA Trans. doi: 10.1016/j.isatra.2019.01.026 – start-page: 226 year: 1996 ident: 10.1016/j.comcom.2021.03.021_b7 article-title: A density-based algorithm for discovering clusters in large spatial databases with noise – volume: 344 start-page: 1492 year: 2014 ident: 10.1016/j.comcom.2021.03.021_b30 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – volume: 32 start-page: 973 year: 2011 ident: 10.1016/j.comcom.2021.03.021_b25 article-title: Apscan: A parameter free algorithm for clustering publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2011.02.001 – volume: 315 start-page: 972 year: 2007 ident: 10.1016/j.comcom.2021.03.021_b29 article-title: Clustering by passing messages between data points publication-title: Science doi: 10.1126/science.1136800 – volume: 9 start-page: 2739 year: 2012 ident: 10.1016/j.comcom.2021.03.021_b12 article-title: Multi-density dbscan algorithm based on density levels partitioning publication-title: J. Inf. Comput. Sci. – volume: 20 start-page: 590 year: 2016 ident: 10.1016/j.comcom.2021.03.021_b17 article-title: An adaptive multipopulation framework for locating and tracking multiple optima publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2015.2504383 – volume: 53 start-page: 1373 year: 2020 ident: 10.1016/j.comcom.2021.03.021_b23 article-title: Bird swarm algorithms with chaotic mapping publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-019-09704-9 – volume: 28 start-page: 45 year: 2016 ident: 10.1016/j.comcom.2021.03.021_b5 article-title: Social big data: Recent achievements and new challenges publication-title: Inf. Fusion doi: 10.1016/j.inffus.2015.08.005 – volume: 28 start-page: 1 year: 2016 ident: 10.1016/j.comcom.2021.03.021_b22 article-title: A new bio-inspired optimisation algorithm: Bird swarm algorithm publication-title: J. Exp. Theor. Artif. Intell. doi: 10.1080/0952813X.2015.1042530 – volume: 10 start-page: 210 year: 2010 ident: 10.1016/j.comcom.2021.03.021_b28 article-title: Heterogeneous density based spatial clustering of application with noise publication-title: Int. J. Comput. Sci. Netw. Secur. – volume: 167 start-page: 63 year: 2021 ident: 10.1016/j.comcom.2021.03.021_b4 article-title: Systematic review on next-generation web-based software architecture clustering models publication-title: Comput. Commun. doi: 10.1016/j.comcom.2020.12.022 – start-page: 473 year: 2011 ident: 10.1016/j.comcom.2021.03.021_b8 article-title: Mr-dbscan: An efficient parallel density-based clustering algorithm using mapreduce – volume: 38 start-page: 9373 year: 2011 ident: 10.1016/j.comcom.2021.03.021_b10 article-title: A new hybrid method based on partitioning-based dbscan and ant clustering publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.01.135 – start-page: 1 year: 2016 ident: 10.1016/j.comcom.2021.03.021_b14 article-title: Particle swarm optimization approach for forecasting backbreak induced by bench blasting publication-title: Neural Comput. Appl. – volume: 3 start-page: 72 year: 2008 ident: 10.1016/j.comcom.2021.03.021_b27 article-title: DDSC: A density differentiated spatial clustering technique publication-title: J. Comput. doi: 10.4304/jcp.3.2.72-79 – volume: 26 start-page: 97 year: 2014 ident: 10.1016/j.comcom.2021.03.021_b2 article-title: Data mining with big data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2013.109 – volume: 275 start-page: 314 year: 2014 ident: 10.1016/j.comcom.2021.03.021_b1 article-title: Data-intensive applications, challenges, techniques and technologies: A survey on big data publication-title: Inform. Sci. doi: 10.1016/j.ins.2014.01.015 – volume: 101 start-page: 265 year: 2011 ident: 10.1016/j.comcom.2021.03.021_b21 article-title: Simulation of dna damage clustering after proton irradiation using an adapted dbscan algorithm publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2010.12.012 – volume: 9 start-page: 2739 year: 2012 ident: 10.1016/j.comcom.2021.03.021_b26 article-title: Multi-density dbscan algorithm based on density levels partitioning publication-title: J. Inf. Comput. Sci. – volume: 9 start-page: 1967 year: 2012 ident: 10.1016/j.comcom.2021.03.021_b11 article-title: Research on adaptive parameters determination in dbscan algorithm publication-title: J. Inf. Comput. Sci. – volume: 13 start-page: 1620 year: 2016 ident: 10.1016/j.comcom.2021.03.021_b3 article-title: A fast density and grid based clustering method for data with arbitrary shapes and noise publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2016.2628747 – volume: 38 start-page: 9373 year: 2011 ident: 10.1016/j.comcom.2021.03.021_b20 article-title: A new hybrid method based on partitioning-based dbscan and ant clustering publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.01.135 – volume: 35 start-page: 57 year: 2019 ident: 10.1016/j.comcom.2021.03.021_b24 article-title: An adaptive bird swarm algorithm with irregular random flight and its application publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2019.06.004 – volume: 104 start-page: 37 year: 2018 ident: 10.1016/j.comcom.2021.03.021_b18 article-title: Adaptive clustering algorithm based on k nn and density publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2018.01.020 – volume: 152 start-page: 119 year: 2020 ident: 10.1016/j.comcom.2021.03.021_b6 article-title: Dynamic clustering method based on power demand and information volume for intelligent and green IoT publication-title: Comput. Commun. doi: 10.1016/j.comcom.2020.01.026 – volume: 25 start-page: 3182 year: 2016 ident: 10.1016/j.comcom.2021.03.021_b19 article-title: Dsets-dbscan: A parameter-free clustering algorithm publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2016.2559803 – volume: 30 start-page: 1477 year: 2009 ident: 10.1016/j.comcom.2021.03.021_b9 article-title: Rough-dbscan: A fast hybrid density based clustering method for large data sets publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2009.08.008 – volume: 133 start-page: 208 year: 2017 ident: 10.1016/j.comcom.2021.03.021_b16 article-title: Adaptive density peak clustering based on k-nearest neighbors with aggregating strategy publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2017.07.010 – volume: 167 start-page: 75 year: 2021 ident: 10.1016/j.comcom.2021.03.021_b13 article-title: A method of two-stage clustering learning based on improved dbscan and density peak algorithm publication-title: Comput. Commun. doi: 10.1016/j.comcom.2020.12.019 |
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| SubjectTerms | Adaptive parameter optimization Bird swarm optimization algorithm DBSCAN Eps parameter |
| Title | A novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm |
| URI | https://dx.doi.org/10.1016/j.comcom.2021.03.021 |
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