A novel Minkowski-distance-based consensus clustering algorithm View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-02

AUTHORS

De-Gang Xu, Pan-Lei Zhao, Chun-Hua Yang, Wei-Hua Gui, Jian-Jun He

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. 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. More... »

PAGES

33-44

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11633-016-1033-z

DOI

http://dx.doi.org/10.1007/s11633-016-1033-z

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https://app.dimensions.ai/details/publication/pub.1026236090


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