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A Clustering Preserving Transformation for k-Means Algorithm Output (2202.10455v2)
Published 19 Feb 2022 in cs.LG
Abstract: This note introduces a novel clustering preserving transformation of cluster sets obtained from $k$-means algorithm. This transformation may be used to generate new labeled data{}sets from existent ones. It is more flexible that 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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