TiK‐means: Transformation‐infusedK‐means clustering for skewed groups
Nicholas Berry, Ranjan S. Maitra · Statistical Analysis and Data Mining The ASA Data Science Journal · 2019
Abstract TheK‐means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK‐means and contributes aK‐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.