http://arxiv.org/abs/1904.09609
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.
N. Berry and R. Maitra
Tue, 23 Apr 19
13/58
Comments: 15 pages, 6 figures, to appear in Statistical Analysis and Data Mining – The ASA Data Science Journal
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