An Efficient K-means Seeding Algorithm

Omar Kettani


This study presents a novel initialization algorithm for the k-means clustering algorithms, which consists to sort the points of a given dataset by both their angles and their norms. The proposed method aims to improve the speed and accuracy of clustering solutions by providing a more informed starting point for the iterative optimization process. Through extensive experiments on a variety of datasets, the proposed algorithm was shown to significantly improve convergence time and result in solutions with higher quality in term of average Silhouette index compared to traditional methods. The results indicate that the proposed initialization technique is a promising alternative for clustering tasks in various domains.

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