TiK-means: $K$-means clustering for skewed groups

Nicholas S. Berry; Ranjan Maitra

doi:10.1002/sam11416

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arXiv · 1904.09609

TiK-means: $K$-means clustering for skewed groups

Nicholas S. Berry·Ranjan Maitra

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Abstract

The $K$-means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK-Means and contributes a $K$-means type algorithm that assigns observations to groups while estimating their skewness-transformation parameters. The resulting groups and transformation reveal general-structured clusters that can be explained by inverting the estimated transformation. Further, a modification of the jump statistic chooses the number of groups. Our algorithm is evaluated on simulated and real-life datasets and then applied to a long-standing astronomical dispute regarding the distinct kinds of gamma ray bursts.

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Nicholas S. Berry, Ranjan Maitra. 2019-04-21. TiK-means: $K$-means clustering for skewed groups. https://doi.org/10.1002/sam11416

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