TiK-means: $K$-means clustering for skewed groups. (arXiv:1904.09609v1 [stat.ML])
<a href="http://arxiv.org/find/stat/1/au:+Berry_N/0/1/0/all/0/1">Nicholas S. Berry</a>, <a href="http://arxiv.org/find/stat/1/au:+Maitra_R/0/1/0/all/0/1">Ranjan Maitra</a>
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.
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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