A Novel Minkowski-distance-based Consensus Clustering Algorithm  

作　　者：De-Gang Xu Pan-Lei Zhao Chun-Hua Yang Wei-Hua Gui Jian-Jun He 

机构地区：[1]College of Information Science and Engineering, Central South University, Changsha 410083, China

出　　处：《International Journal of Automation and computing》2017年第1期33-44,共12页国际自动化与计算杂志（英文版）

基　　金：supported by National High Technology Research and Development Program(863Program)(No.2013AA040301-3);National Natural Science Foundation of China(Nos.61473319 and 61104135);the Key Project of National Natural Science Foundation of China(Nos.61621062 and 61134006);the Innovation Research Funds of Central South University(No.2016CX014)

摘　　要：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.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.

关 键 词：Minkowski distance consensus clustering similarity matrix process data froth flotation. 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]