Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

6-2019

Journal or Book Title

Statistical Analysis and Data Mining

Volume

12

Issue

3

First Page

223

Last Page

233

DOI

10.1002/sam.11416

Abstract

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.

Comments

This is the peer-reviewed version of the following article: Berry, Nicholas S., and Ranjan Maitra. "TiK‐means: Transformation‐infused K‐means clustering for skewed groups." Statistical Analysis and Data Mining: The ASA Data Science Journal 12, no. 3 (2019): 223-233, which has been published in final form at DOI: 10.1002/sam.11416. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Posted with permission.

Copyright Owner

Wiley Periodicals, Inc.

Language

en

File Format

application/pdf

Published Version

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