نویسندگان | Javad Etminan, |
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نشریه | Communications in Statistics Part B: Simulation and Computation |
شماره صفحات | 1595-1605 |
شماره سریال | 48 |
شماره مجلد | 6 |
ضریب تاثیر (IF) | 0.457 |
نوع مقاله | Full Paper |
تاریخ انتشار | 2019 |
رتبه نشریه | ISI |
نوع نشریه | چاپی |
کشور محل چاپ | ایران |
نمایه نشریه | JCR،Scopus |
چکیده مقاله
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