A Pitman measure of similarity in k-means for clustering heavy-tailed data. Issue 6 (3rd July 2019)

• Record Type: Journal Article
• Authors: Reybod, Arman; Etminan, Javad; Mohammadpour, Adel
• Is Part Of: Communications in statistics. Volume 48:Issue 6(2019)
• Page Start: 1595
• Page End: 1605
• Publication Date: 2019-07-03
• Subjects: α-stable distributions -- α-sub-Gaussian distributions -- Heavy tail distributions -- k-means clustering -- Pitman measure of closeness; 62H30; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2017.1419259
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: One of the most popular methods and algorithms to partition data to k clusters is k -means clustering algorithm. Since this method relies on some basic conditions such as, the existence of mean and finite variance, it is unsuitable for data that their variances are infinite such as data with heavy tailed distribution. Pitman Measure of Closeness (PMC) is a criterion to show how much an estimator is close to its parameter with respect to another estimator. In this article using PMC, based on k -means clustering, a new distance and clustering algorithm is developed for heavy tailed data.