Enhanced Initial Centroids for K-means Algorithm
Автор: Aleta C. Fabregas, Bobby D. Gerardo, Bartolome T. Tanguilig III
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 1 Vol. 9, 2017 года.
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This paper focuses on the enhanced initial centroids for the K-means algorithm. The original k-means is using the random choice of initial seeds which is a major limitation of the original K-means algorithm because it produces less reliable result of clustering the data. The enhanced method of the k-means algorithm includes the computation of the weighted mean to improve the centroids initialization. This paper shows the comparison between K-Means and the enhanced K-Means algorithm, and it proves that the new method of selecting initial seeds is better in terms of mathematical computation and reliability.
K-means algorithm, Euclidian Distance, Centroids, Clustering, Enhanced-K-means algorithm Weighted Average mean
Короткий адрес: https://sciup.org/15012608
IDR: 15012608
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