Attainment of K-Means Algorithm using Hellinger distance
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Ovidius University Press
2017-01-01
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| Series: | Ovidius University Annals: Economic Sciences Series |
| Subjects: | |
| Online Access: | http://stec.univ-ovidius.ro/html/anale/RO/2017-2/Section%20III/31.pdf |
| Summary: | 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. |
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| ISSN: | 2393-3127 2393-3127 |