A Novel Minkowski-distance-based Consensus Clustering Algorithm
Consensus clustering is the problem of coordinating clustering information about the same data set coming from different runs of the same algorithm. Consensus clustering is becoming a state-of-the-art approach in an increasing number of applications. However, determining the optimal cluster number i...
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| Veröffentlicht in: | International journal of automation and computing 2017-02, Vol.14 (1), p.33-44 |
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| container_title | International journal of automation and computing |
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| creator | Xu, De-Gang Zhao, Pan-Lei Yang, Chun-Hua Gui, Wei-Hua He, Jian-Jun |
| description | Consensus clustering is the problem of coordinating clustering information about the same data set coming from different runs of the same algorithm. Consensus clustering is becoming a state-of-the-art approach in an increasing number of applications. However, determining the optimal cluster number is still an open problem. In this paper, we propose a novel consensus clustering algorithm that is based on the Minkowski distance. Fusing with the Newman greedy algorithm in complex networks, the proposed clustering algorithm can automatically set the number of clusters. It is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. A numerical simulation is also given to demonstrate the effectiveness of the proposed algorithm. Finally, this consensus clustering algorithm is applied to a froth flotation process. |
| doi_str_mv | 10.1007/s11633-016-1033-z |
| format | Article |
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J. Autom. Comput</addtitle><addtitle>International Journal of Automation and computing</addtitle><description>Consensus clustering is the problem of coordinating clustering information about the same data set coming from different runs of the same algorithm. Consensus clustering is becoming a state-of-the-art approach in an increasing number of applications. However, determining the optimal cluster number is still an open problem. In this paper, we propose a novel consensus clustering algorithm that is based on the Minkowski distance. Fusing with the Newman greedy algorithm in complex networks, the proposed clustering algorithm can automatically set the number of clusters. It is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. A numerical simulation is also given to demonstrate the effectiveness of the proposed algorithm. 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J. Autom. Comput</stitle><addtitle>International Journal of Automation and computing</addtitle><date>2017-02-01</date><risdate>2017</risdate><volume>14</volume><issue>1</issue><spage>33</spage><epage>44</epage><pages>33-44</pages><issn>1476-8186</issn><issn>2153-182X</issn><eissn>1751-8520</eissn><eissn>2153-1838</eissn><abstract>Consensus clustering is the problem of coordinating clustering information about the same data set coming from different runs of the same algorithm. Consensus clustering is becoming a state-of-the-art approach in an increasing number of applications. However, determining the optimal cluster number is still an open problem. In this paper, we propose a novel consensus clustering algorithm that is based on the Minkowski distance. Fusing with the Newman greedy algorithm in complex networks, the proposed clustering algorithm can automatically set the number of clusters. 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| subjects | Algorithms Automation CAE) and Design Cluster analysis Clustering Computer Applications Computer-Aided Engineering (CAD Control Data processing Datasets Eigenvalues Engineering Flotation Greedy algorithms Mechatronics Methods Noise sensitivity Processing industry R&D Research & development Research Article Robotics 一致性 复杂网络 聚类算法 自动设置 贪婪算法 距离 闵可夫斯基 集成解决方案 |
| title | A Novel Minkowski-distance-based Consensus Clustering Algorithm |
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