TiK‐means: Transformation‐infused  K ‐means clustering for skewed groups | Zendy

Berry Nicholas S. | Zendy; Maitra Ranjan | Zendy

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TiK‐means: Transformation‐infused K ‐means clustering for skewed groups

Author(s) -

Berry Nicholas S.,

Maitra Ranjan

Publication year - 2019

Publication title -

statistical analysis and data mining: the asa data science journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.381

H-Index - 33

eISSN - 1932-1872

pISSN - 1932-1864

DOI - 10.1002/sam.11416

Subject(s) - skewness , transformation (genetics) , statistic , cluster analysis , algorithm , mathematics , jump , group (periodic table) , computer science , statistics , physics , biochemistry , chemistry , quantum mechanics , gene

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 data sets and then applied to a long‐standing astronomical dispute regarding the distinct kinds of gamma ray bursts.

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