K-RMS Algorithm
| Autor: | Dipankar Das, Avishek Garain |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences Unsupervised learning 020206 networking & telecommunications 020201 artificial intelligence & image processing 02 engineering and technology Cluster analysis Algorithm General Environmental Science Domain (software engineering) |
| Zdroj: | Procedia Computer Science. 167:113-120 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2020.03.188 |
| Popis: | Clustering is an unsupervised learning problem in the domain of machine learning and data science, where information about data instances may or may not be given. K-Means algorithm is one such clustering algorithms, the use of which is widespread. But, at the same time K-Means suffers from a few disadvantages such as low accuracy and high number of iterations. In order to rectify such problems, a modified K-Means algorithm has been demonstrated, named as K-RMS clustering algorithm in the present work. The modifications have been done so that the accuracy increases albeit with less number of iterations and specially performs well for decimal data compared to K-Means. The modified algorithm has been tested on 12 datasets obtained from UCI web archive, and the results gathered are very promising. |
| Databáze: | OpenAIRE |
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