Differential evolution-based transfer rough clustering algorithm
Abstract Due to well processing the uncertainty in data, rough clustering methods have been successfully applied in many fields. However, when the capacity of the available data is limited or the data are disturbed by noise, the rough clustering algorithms always cannot effectively explore the struc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-02-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-00987-8 |
| _version_ | 1797675007831179264 |
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| author | Feng Zhao Chaofei Wang Hanqiang Liu |
| author_facet | Feng Zhao Chaofei Wang Hanqiang Liu |
| author_sort | Feng Zhao |
| collection | DOAJ |
| description | Abstract Due to well processing the uncertainty in data, rough clustering methods have been successfully applied in many fields. However, when the capacity of the available data is limited or the data are disturbed by noise, the rough clustering algorithms always cannot effectively explore the structure of the data. Furthermore, rough clustering algorithms are usually sensitive to the initialized cluster centers and easy to fall into local optimum. To resolve the problems mentioned above, a novel differential evolution-based transfer rough clustering (DE-TRC) algorithm is proposed in this paper. First, transfer learning mechanism is introduced into rough clustering and a transfer rough clustering framework is designed, which utilizes the knowledge from the related domain to assist the clustering task. Then, the objective function of the transfer rough clustering algorithm is optimized by using the differential evolution algorithm to enhance the robustness of the algorithm. It can overcome the sensitivity to initialized cluster centers and meanwhile achieve the global optimal clustering. The proposed algorithm is validated on different synthetic and real-world datasets. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison with both traditional rough clustering algorithms and other state-of-the-art clustering algorithms. |
| first_indexed | 2024-03-11T22:08:29Z |
| format | Article |
| id | doaj.art-fb3802d71f1645afb9bf4b52aeff7a6a |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-11T22:08:29Z |
| publishDate | 2023-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-fb3802d71f1645afb9bf4b52aeff7a6a2023-09-24T11:35:18ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-02-01955033504710.1007/s40747-023-00987-8Differential evolution-based transfer rough clustering algorithmFeng Zhao0Chaofei Wang1Hanqiang Liu2School of Communications and Information Engineering, Xi’an University of Posts and TelecommunicationsSchool of Communications and Information Engineering, Xi’an University of Posts and TelecommunicationsSchool of Computer Science, Shaanxi Normal UniversityAbstract Due to well processing the uncertainty in data, rough clustering methods have been successfully applied in many fields. However, when the capacity of the available data is limited or the data are disturbed by noise, the rough clustering algorithms always cannot effectively explore the structure of the data. Furthermore, rough clustering algorithms are usually sensitive to the initialized cluster centers and easy to fall into local optimum. To resolve the problems mentioned above, a novel differential evolution-based transfer rough clustering (DE-TRC) algorithm is proposed in this paper. First, transfer learning mechanism is introduced into rough clustering and a transfer rough clustering framework is designed, which utilizes the knowledge from the related domain to assist the clustering task. Then, the objective function of the transfer rough clustering algorithm is optimized by using the differential evolution algorithm to enhance the robustness of the algorithm. It can overcome the sensitivity to initialized cluster centers and meanwhile achieve the global optimal clustering. The proposed algorithm is validated on different synthetic and real-world datasets. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison with both traditional rough clustering algorithms and other state-of-the-art clustering algorithms.https://doi.org/10.1007/s40747-023-00987-8Rough clusteringTransfer learningTransfer rough clusteringDifferential evolutionPrototype transfer |
| spellingShingle | Feng Zhao Chaofei Wang Hanqiang Liu Differential evolution-based transfer rough clustering algorithm Complex & Intelligent Systems Rough clustering Transfer learning Transfer rough clustering Differential evolution Prototype transfer |
| title | Differential evolution-based transfer rough clustering algorithm |
| title_full | Differential evolution-based transfer rough clustering algorithm |
| title_fullStr | Differential evolution-based transfer rough clustering algorithm |
| title_full_unstemmed | Differential evolution-based transfer rough clustering algorithm |
| title_short | Differential evolution-based transfer rough clustering algorithm |
| title_sort | differential evolution based transfer rough clustering algorithm |
| topic | Rough clustering Transfer learning Transfer rough clustering Differential evolution Prototype transfer |
| url | https://doi.org/10.1007/s40747-023-00987-8 |
| work_keys_str_mv | AT fengzhao differentialevolutionbasedtransferroughclusteringalgorithm AT chaofeiwang differentialevolutionbasedtransferroughclusteringalgorithm AT hanqiangliu differentialevolutionbasedtransferroughclusteringalgorithm |