An Empirical Evaluation of Different Initializations on the Number of K-Means Iterations
Cordeiro De Amorim, Renato
(2013)
An Empirical Evaluation of Different Initializations on the Number of K-Means Iterations.
Springer Nature.
This paper presents an analysis of the number of iterations K-Means takes to converge under different initializations. We have experimented with seven initialization algorithms in a total of 37 real and synthetic datasets. We have found that hierarchical-based initializations tend to be most effective at reducing the number of iterations, especially a divisive algorithm using the Ward criterion when applied to real datasets
Item Type | Other |
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Date Deposited | 29 May 2025 09:05 |
Last Modified | 29 May 2025 09:05 |