A Clustering Preserving Transformation for k-Means Algorithm Output
This work addresses a specific issue in data augmentation for clustering, but it appears incremental as it builds on existing transformations without demonstrating broad impact.
The paper tackles the problem of generating new labeled datasets from existing ones by introducing a clustering preserving transformation for k-means outputs, which allows moving data points within and between clusters more flexibly than Kleinberg's consistency transformation.
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.