Abstract
A novel clustering preserving transformation of cluster sets obtained from k-means algorithm is introduced. This transformation may be used to generate new labeled datasets from existent ones. It is more flexible than Kleinberg axiom based consistency transformation because data points in a cluster can be moved away and datapoints between clusters may come closer together.
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Kłopotek, M.A. (2022). A New Clustering Preserving Transformation for k-Means Algorithm Output. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_30
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