A novel Minkowski-distance-based consensus clustering algorithm

作者：Xu, De-Gang^*; Zhao, Pan-Lei; Yang, Chun-Hua; Gui, Wei-Hua; He, Jian-Jun

来源：International Journal of Automation and Computing, 2017, 14(1): 33-44.

DOI：10.1007/s11633-016-1033-z

• Clustering information
• Consensus clustering
• Froth flotation process
• Minkowski distance
• Optimal cluster number
• Process data
• Similarity matrix
• State-of-the-art approach

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.

• 出版日期2017-2-1
• 单位中南大学