Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applications
As density-based algorithm, Density Peak Clustering (DPC) algorithm has superiority of clustering by finding the density peaks. But the cut-off distance and clustering centres had to be set at random, which would influence clustering outcomes. Fruit flies find the best food by local searching and gl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2017-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/265180 |
| _version_ | 1797207916815581184 |
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| author | Ruihong Zhou Qiaoming Liu Zhengliang Xu Limin Wang Xuming Han |
| author_facet | Ruihong Zhou Qiaoming Liu Zhengliang Xu Limin Wang Xuming Han |
| author_sort | Ruihong Zhou |
| collection | DOAJ |
| description | As density-based algorithm, Density Peak Clustering (DPC) algorithm has superiority of clustering by finding the density peaks. But the cut-off distance and clustering centres had to be set at random, which would influence clustering outcomes. Fruit flies find the best food by local searching and global searching. The food found was the parameter extreme value calculated by Fruit Fly Optimization Algorithm (FOA). Based on the rapid search and fast convergence superiorities of FOA, it is possible to make up the casualness of DPC. An improved fruit fly optimization-based density peak clustering algorithm was proposed as FOA-DPC. The FOA-DPC algorithm would be more efficient and effective than DPC algorithm. The results of seven simulation experiments in UCI data sets validated that the proposed algorithm did not only have better clustering performance, but also were closer to the true clustering numbers. Furthermore, FOA-DPC was applied to practical financial data analysis and the conclusion was also effective. |
| first_indexed | 2024-04-24T09:30:31Z |
| format | Article |
| id | doaj.art-f52c4373eda94677a06887dded364250 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:30:31Z |
| publishDate | 2017-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-f52c4373eda94677a06887dded3642502024-04-15T14:08:50ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392017-01-0124247348010.17559/TV-20170303013036Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applicationsRuihong Zhou0Qiaoming Liu1Zhengliang Xu2Limin Wang3Xuming Han4School of Management, Jilin University, School of Management Science and Information Engineering, Jilin University of Finance and Economics, 3699 Jingyue Street, Changchun 130117, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun 130012, ChinaSchool of Management, Jilin University, 2699 Qianjin Street, Changchun 130012, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, 3699 Jingyue Street, Changchun 130117, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, 2055 Yanan Street, Changchun 130012, ChinaAs density-based algorithm, Density Peak Clustering (DPC) algorithm has superiority of clustering by finding the density peaks. But the cut-off distance and clustering centres had to be set at random, which would influence clustering outcomes. Fruit flies find the best food by local searching and global searching. The food found was the parameter extreme value calculated by Fruit Fly Optimization Algorithm (FOA). Based on the rapid search and fast convergence superiorities of FOA, it is possible to make up the casualness of DPC. An improved fruit fly optimization-based density peak clustering algorithm was proposed as FOA-DPC. The FOA-DPC algorithm would be more efficient and effective than DPC algorithm. The results of seven simulation experiments in UCI data sets validated that the proposed algorithm did not only have better clustering performance, but also were closer to the true clustering numbers. Furthermore, FOA-DPC was applied to practical financial data analysis and the conclusion was also effective.https://hrcak.srce.hr/file/265180clustering centrescut-off distanceDensity Peak Clustering (DPC)Fruit Fly Optimization |
| spellingShingle | Ruihong Zhou Qiaoming Liu Zhengliang Xu Limin Wang Xuming Han Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applications Tehnički Vjesnik clustering centres cut-off distance Density Peak Clustering (DPC) Fruit Fly Optimization |
| title | Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applications |
| title_full | Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applications |
| title_fullStr | Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applications |
| title_full_unstemmed | Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applications |
| title_short | Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applications |
| title_sort | improved fruit fly optimization algorithm based density peak clustering and its applications |
| topic | clustering centres cut-off distance Density Peak Clustering (DPC) Fruit Fly Optimization |
| url | https://hrcak.srce.hr/file/265180 |
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