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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Bibliographic Details
Published inInternational journal of automation and computing Vol. 14; no. 1; pp. 33 - 44
Main Authors Xu, De-Gang, Zhao, Pan-Lei, Yang, Chun-Hua, Gui, Wei-Hua, He, Jian-Jun
Format Journal Article
LanguageEnglish
Published Beijing Institute of Automation, Chinese Academy of Sciences 01.02.2017
Springer Nature B.V
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
Minkowski distance
process data
froth flotation
consensus clustering
similarity matrix
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Summary: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.
Bibliography: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.
Minkowski distance, consensus clustering, similarity matrix, process data, froth flotation.
11-5350/TP
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1476-8186
2153-182X
1751-8520
2153-1838
DOI:10.1007/s11633-016-1033-z