Abstract
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.
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Płoński, P., Zaremba, K. (2013). Full and Semi-supervised k-Means Clustering Optimised by Class Membership Hesitation. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_23
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DOI: https://doi.org/10.1007/978-3-642-37213-1_23
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