A Review on Consensus Clustering Methods

Petros Xanthopoulos ()
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Petros Xanthopoulos: University of Central Florida, Industrial Engineering and Management Systems Department

A chapter in Optimization in Science and Engineering, 2014, pp 553-566 from Springer

Abstract: Abstract Unsupervised learning/clustering is one of the most common, yet computationally intense, data analysis problems in data mining. The plethora of clustering algorithms and performance measures makes the choice of optimal clustering algorithm a challenging task. In order to overcome this shortcoming consensus learning methods have been proposed in the literature. These methods try to optimally combine independently obtained clusterings into a single more robust clustering of improved quality. In this chapter we provide a review of unsupervised consensus learning techniques based on their underlying theoretical principles. We present the exact, approximation, and heuristic approaches, the relation of consensus clustering with other well-studied problems, and discuss relevant applications.

Keywords: Consensus Clustering; Correlation Clustering Problem; Nonnegative Matrix Factorization (NNMF); Consensus Graph; NNMF Problem (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4939-0808-0_26

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DOI: 10.1007/978-1-4939-0808-0_26

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