Contrastive Study of Distributed Multitask Fuzzy C-means Clustering and Traditional Clustering Algorithms
Clustering has been widely used in every field,but traditional classical clustering unable to handle multiple tasks at the same time under the scenario of data sets.Comparing with the classical clustering algorithm,the clustering results of Distributed Multitask Fuzzy C-means Clustering(MT-FCM) and...
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| Vydáno v: | 2020 5th International Conference on Communication, Image and Signal Processing (CCISP) s. 239 - 245 |
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01.11.2020
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| Abstract | Clustering has been widely used in every field,but traditional classical clustering unable to handle multiple tasks at the same time under the scenario of data sets.Comparing with the classical clustering algorithm,the clustering results of Distributed Multitask Fuzzy C-means Clustering(MT-FCM) and traditional clustering algorithms can be verified from different multitask scenarios according to the features of MT-FCM algorithm.MT-FCM algorithm is suitable for multitask environment with a moderate number of tasks,in which case the clustering effect is better than that of other traditional clustering algorithms,but there are also non-applicable scenarios by the analysis of experimental results.This experiment not only summarizes and improves the characteristics of the MT-FCM algorithm,but also finds out the shortcomings of the algorithm,which provides a valuable reference for the follow-up research of Distributed Multitask Fuzzy C-means Clustering. |
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| AbstractList | Clustering has been widely used in every field,but traditional classical clustering unable to handle multiple tasks at the same time under the scenario of data sets.Comparing with the classical clustering algorithm,the clustering results of Distributed Multitask Fuzzy C-means Clustering(MT-FCM) and traditional clustering algorithms can be verified from different multitask scenarios according to the features of MT-FCM algorithm.MT-FCM algorithm is suitable for multitask environment with a moderate number of tasks,in which case the clustering effect is better than that of other traditional clustering algorithms,but there are also non-applicable scenarios by the analysis of experimental results.This experiment not only summarizes and improves the characteristics of the MT-FCM algorithm,but also finds out the shortcomings of the algorithm,which provides a valuable reference for the follow-up research of Distributed Multitask Fuzzy C-means Clustering. |
| Author | Zi, Yuan Jiang, Yizhang Guo, Yanlin |
| Author_xml | – sequence: 1 givenname: Yanlin surname: Guo fullname: Guo, Yanlin email: 6191663001@stu.jiangnan.edu.cn organization: Jiangnan University,School of Artificial Intelligence and Computer Science,Wuxi,People's Republic of China – sequence: 2 givenname: Yuan surname: Zi fullname: Zi, Yuan email: 3478814759@qq.com organization: Jiangnan University,School of Artificial Intelligence and Computer Science,Wuxi,People's Republic of China – sequence: 3 givenname: Yizhang surname: Jiang fullname: Jiang, Yizhang email: yzjiang@jiangnan.edu.cn organization: Jiangnan University,School of Artificial Intelligence and Computer Science,Wuxi,People's Republic of China |
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| Snippet | Clustering has been widely used in every field,but traditional classical clustering unable to handle multiple tasks at the same time under the scenario of data... |
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| SubjectTerms | Clustering algorithms comparison Distributed databases Distributed Multitask Fuzzy C-means Clustering Indexes Iterative algorithms Linear programming multitask learning Phase change materials Task analysis traditional clustering algorithms |
| Title | Contrastive Study of Distributed Multitask Fuzzy C-means Clustering and Traditional Clustering Algorithms |
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