Attainment of K-Means Algorithm using Hellinger distance

Stancu Ana-Maria Ramona (), Marian Pompiliu Cristescu () and Stoica Liviu Constantin ()
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Stancu Ana-Maria Ramona: “Dimitrie Cantemir†Christian University
Stoica Liviu Constantin: Academy of Economic Studies, Bucharest

Ovidius University Annals, Economic Sciences Series, 2017, vol. XVII, issue 2, 324-329

Abstract: In this article in the first part I will begin with an introduction to unsupervised learning methods, focusing on the K-Means clustering algorithm, which is achieved with the help of the Euclidian distance. In the second part we modified the K-Means algorithm, that is, it was achieved with the help of the Hellinger distance, after which the clustering time was compared and a parallel was made between the two algorithms (the K-Means algorithm achieved with the Euclidean distance and the K-Means algorithm achieved with Hellinger distance). As a result of the two algorithms I found that the number of groups is the same, and the number of iterations is different.

Keywords: algorithm; cluster; distance; iteration; group (search for similar items in EconPapers)
JEL-codes: O31 O32 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:ovi:oviste:v:xvii:y:2017:i:2:p:324-329

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