Authors | Javad Etminan, |
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Journal | Communications in Statistics Part B: Simulation and Computation |
Page number | 1595-1605 |
Serial number | 48 |
Volume number | 6 |
IF | 0.457 |
Paper Type | Full Paper |
Published At | 2019 |
Journal Grade | ISI |
Journal Type | Typographic |
Journal Country | Iran, Islamic Republic Of |
Journal Index | JCR،Scopus |
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.
tags: α-stable distributions; α-sub-Gaussian distributions; Heavy tail distributions; k-means clustering; Pitman measure of closeness