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The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning

Cordeiro de Amorim, R and Shestakov, A and Mirkin, B and Makarenkov, V (2017) 'The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning.' Pattern Recognition, 67. 62 - 72. ISSN 0031-3203

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Abstract

The Minkowski weighted K-means (MWK-means) is a recently developed clustering algorithm capable of computing feature weights. The cluster-specific weights in MWK-means follow the intuitive idea that a feature with low variance should have a greater weight than a feature with high variance. The final clustering found by this algorithm depends on the selection of the Minkowski distance exponent. This paper explores the possibility of using the central Minkowski partition in the ensemble of all Minkowski partitions for selecting an optimal value of the Minkowski exponent. The central Minkowski partition appears to be also a good consensus partition. Furthermore, we discovered some striking correlation results between the Minkowski profile, defined as a mapping of the Minkowski exponent values into the average similarity values of the optimal Minkowski partitions, and the Adjusted Rand Index vectors resulting from the comparison of the obtained partitions to the ground truth. Our findings were confirmed by a series of computational experiments involving synthetic Gaussian clusters and real-world data.

Item Type: Article
Uncontrolled Keywords: Clustering Central clustering Feature weighting Minkowski metric Minkowski ensemble
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 18 Sep 2017 12:26
Last Modified: 18 Sep 2017 13:15
URI: http://repository.essex.ac.uk/id/eprint/20362

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