Data Clustering Using by Chaotic SSPCO Algorithm
Data clustering is a popular analysis tool for data statistics in several fields, including includes pattern recognition, data mining, machine learning, image analysis and bioinformatics, in which the information to be analyzed can be of any distribution in size and shape. Clustering is effective as...
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OICC Press
2024-02-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/4780 |
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| description | Data clustering is a popular analysis tool for data statistics in several fields, including includes pattern recognition, data mining, machine learning, image analysis and bioinformatics, in which the information to be analyzed can be of any distribution in size and shape. Clustering is effective as a technique for discerning the structure of and unraveling the complex relationship between massive amounts of data. See-See partridge chickâs optimization (SSPCO) algorithm is a new optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. We propose chaotic map SSPCO optimization method for clustering, which uses a chaotic map to adopt a random sequence with a random starting point as a parameter, the method relies on this parameter to update the positions and velocities of the chicks. In the study, twelve different clustering algorithms were extensively compared on thirteen test data sets. The results indicate that the performance of the Chaotic SSPCO method is significantly better than the performance of other algorithms for data clustering problems. |
| format | Article |
| id | doaj-art-e778116d38f940ccb55e75378dfc74e8 |
| institution | OA Journals |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-e778116d38f940ccb55e75378dfc74e82025-08-20T01:47:44ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962024-02-01112Data Clustering Using by Chaotic SSPCO AlgorithmOICC Press Authors0Various OICC Press AuthorsData clustering is a popular analysis tool for data statistics in several fields, including includes pattern recognition, data mining, machine learning, image analysis and bioinformatics, in which the information to be analyzed can be of any distribution in size and shape. Clustering is effective as a technique for discerning the structure of and unraveling the complex relationship between massive amounts of data. See-See partridge chickâs optimization (SSPCO) algorithm is a new optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. We propose chaotic map SSPCO optimization method for clustering, which uses a chaotic map to adopt a random sequence with a random starting point as a parameter, the method relies on this parameter to update the positions and velocities of the chicks. In the study, twelve different clustering algorithms were extensively compared on thirteen test data sets. The results indicate that the performance of the Chaotic SSPCO method is significantly better than the performance of other algorithms for data clustering problems.https://oiccpress.com/mjee/article/view/4780ChaoticClusteringClustering Error. DatasetSSPCO Algorithm |
| spellingShingle | OICC Press Authors Data Clustering Using by Chaotic SSPCO Algorithm Majlesi Journal of Electrical Engineering Chaotic Clustering Clustering Error. Dataset SSPCO Algorithm |
| title | Data Clustering Using by Chaotic SSPCO Algorithm |
| title_full | Data Clustering Using by Chaotic SSPCO Algorithm |
| title_fullStr | Data Clustering Using by Chaotic SSPCO Algorithm |
| title_full_unstemmed | Data Clustering Using by Chaotic SSPCO Algorithm |
| title_short | Data Clustering Using by Chaotic SSPCO Algorithm |
| title_sort | data clustering using by chaotic sspco algorithm |
| topic | Chaotic Clustering Clustering Error. Dataset SSPCO Algorithm |
| url | https://oiccpress.com/mjee/article/view/4780 |
| work_keys_str_mv | AT oiccpressauthors dataclusteringusingbychaoticsspcoalgorithm |