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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Full and Semi-supervised k-Means Clustering Optimised by Class Membership Hesitation
verfasst von : Piotr Płoński, Krzysztof Zaremba
Erschienen in: Adaptive and Natural Computing Algorithms
Verlag: Springer Berlin Heidelberg
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K-Means algorithm is one of the most popular methods for cluster analysis. K-Means, as the majority of clustering methods optimise clusters in an unsupervised way. In this paper we present a method of cluster’s class membership hesitation, which enables k-Means to learn with fully and partially labelled data. In the proposed method the hesitation of cluster during optimisation step is controlled by Metropolis-Hastings algorithm. The proposed method was compared with state-of-art methods for supervised and semi-supervised clustering on benchmark data sets. Obtained results yield the same or better classification accuracy on both types of supervision.