A Multiclustering Evolutionary Hyperrectangle-Based Algorithm
Abstract Clustering is a grouping technique that has long been used to relate data homogeneously. With the huge growth of complex datasets from different sources in the last decade, new paradigms have emerged. Multiclustering is a new concept within clustering that attempts to simultaneously generat...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-10-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00341-3 |
| _version_ | 1827708408425349120 |
|---|---|
| author | Luis Alfonso Pérez Martos Ángel Miguel García-Vico Pedro González Cristóbal J. Carmona del Jesus |
| author_facet | Luis Alfonso Pérez Martos Ángel Miguel García-Vico Pedro González Cristóbal J. Carmona del Jesus |
| author_sort | Luis Alfonso Pérez Martos |
| collection | DOAJ |
| description | Abstract Clustering is a grouping technique that has long been used to relate data homogeneously. With the huge growth of complex datasets from different sources in the last decade, new paradigms have emerged. Multiclustering is a new concept within clustering that attempts to simultaneously generate multiple clusters that are bound to be different from each other, allowing to analyze and discover hidden patterns in the dataset compared to single clustering methods. This paper presents a hybrid methodology based on an evolutionary approach with the concepts of hyperrectangle for multiclustering, called MultiCHCClust. The algorithm is applied in a post-processing stage and it improves the results obtained for a clustering algorithm with respect to the partitioning of the dataset and the optimization of the number of partitions, achieving a high degree of compactness and separation of the partitioned dataset as can be observed in a complete experimental study. |
| first_indexed | 2024-03-10T17:04:15Z |
| format | Article |
| id | doaj.art-c05efd9299dc4f5885c0210de6a242d1 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-10T17:04:15Z |
| publishDate | 2023-10-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-c05efd9299dc4f5885c0210de6a242d12023-11-20T10:52:24ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-10-0116111910.1007/s44196-023-00341-3A Multiclustering Evolutionary Hyperrectangle-Based AlgorithmLuis Alfonso Pérez Martos0Ángel Miguel García-Vico1Pedro González2Cristóbal J. Carmona del Jesus3Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of JaenAndalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of JaenAndalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of JaenAndalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of JaenAbstract Clustering is a grouping technique that has long been used to relate data homogeneously. With the huge growth of complex datasets from different sources in the last decade, new paradigms have emerged. Multiclustering is a new concept within clustering that attempts to simultaneously generate multiple clusters that are bound to be different from each other, allowing to analyze and discover hidden patterns in the dataset compared to single clustering methods. This paper presents a hybrid methodology based on an evolutionary approach with the concepts of hyperrectangle for multiclustering, called MultiCHCClust. The algorithm is applied in a post-processing stage and it improves the results obtained for a clustering algorithm with respect to the partitioning of the dataset and the optimization of the number of partitions, achieving a high degree of compactness and separation of the partitioned dataset as can be observed in a complete experimental study.https://doi.org/10.1007/s44196-023-00341-3Evolutionary algorithmClusteringMulticlusteringData mining |
| spellingShingle | Luis Alfonso Pérez Martos Ángel Miguel García-Vico Pedro González Cristóbal J. Carmona del Jesus A Multiclustering Evolutionary Hyperrectangle-Based Algorithm International Journal of Computational Intelligence Systems Evolutionary algorithm Clustering Multiclustering Data mining |
| title | A Multiclustering Evolutionary Hyperrectangle-Based Algorithm |
| title_full | A Multiclustering Evolutionary Hyperrectangle-Based Algorithm |
| title_fullStr | A Multiclustering Evolutionary Hyperrectangle-Based Algorithm |
| title_full_unstemmed | A Multiclustering Evolutionary Hyperrectangle-Based Algorithm |
| title_short | A Multiclustering Evolutionary Hyperrectangle-Based Algorithm |
| title_sort | multiclustering evolutionary hyperrectangle based algorithm |
| topic | Evolutionary algorithm Clustering Multiclustering Data mining |
| url | https://doi.org/10.1007/s44196-023-00341-3 |
| work_keys_str_mv | AT luisalfonsoperezmartos amulticlusteringevolutionaryhyperrectanglebasedalgorithm AT angelmiguelgarciavico amulticlusteringevolutionaryhyperrectanglebasedalgorithm AT pedrogonzalez amulticlusteringevolutionaryhyperrectanglebasedalgorithm AT cristobaljcarmonadeljesus amulticlusteringevolutionaryhyperrectanglebasedalgorithm AT luisalfonsoperezmartos multiclusteringevolutionaryhyperrectanglebasedalgorithm AT angelmiguelgarciavico multiclusteringevolutionaryhyperrectanglebasedalgorithm AT pedrogonzalez multiclusteringevolutionaryhyperrectanglebasedalgorithm AT cristobaljcarmonadeljesus multiclusteringevolutionaryhyperrectanglebasedalgorithm |