A novel Minkowski-distance-based consensus clustering algorithm

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• 作者：De-Gang Xu ; Pan-Lei Zhao ; Chun-Hua Yang…
• 关键词：Minkowski distance ; consensus clustering ; similarity matrix ; process data ; froth flotation
• 刊名：International Journal of Automation and Computing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：14
• 期：1
• 页码：33-44
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Control, Robotics, Mechatronics; Computer Applications; Computer-Aided Engineering (CAD, CAE) and Design;
• 出版者：Institute of Automation, Chinese Academy of Sciences
• ISSN：1751-8520
• 卷排序：14

文摘

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.