A Clustering Preserving Transformation for k-Means Algorithm Output
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 datasets 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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