TiK-means: $K$-means clustering for skewed groups
Machine Learning
2019-05-21 v1 High Energy Astrophysical Phenomena
Computer Vision and Pattern Recognition
Machine Learning
Applications
Methodology
Abstract
The -means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK-Means and contributes a -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.
Keywords
Cite
@article{arxiv.1904.09609,
title = {TiK-means: $K$-means clustering for skewed groups},
author = {Nicholas S. Berry and Ranjan Maitra},
journal= {arXiv preprint arXiv:1904.09609},
year = {2019}
}
Comments
15 pages, 6 figures, to appear in Statistical Analysis and Data Mining - The ASA Data Science Journal