A Note on the Effectiveness of the Least Squares Consensus Clustering

Mirkin, Boris; Shestakov, Andrey

doi:10.1007/978-1-4939-0742-7_11

Cited by 2 publications

(1 citation statement)

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“…Therefore, consensus partition should be one of those constituting the Minkowski partition ensemble. Indeed, we have tried building consensus partitions by using an algorithm from [2,37], which is a version of the approach described in [17]. This usually led to different partitions indeed, but with quite a mediocre cluster recovery results.…”

Section: Minkowski Partition Ensemble Minkowski Profile and Their Usementioning

confidence: 99%

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

Amorim

Shestakov

Mirkin

2017

Pattern Recognition

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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.

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Section: Minkowski Partition Ensemble Minkowski Profile and Their Usementioning

confidence: 99%