ck-means and fck-means: Two Deterministic Initialization Procedures for k-means Algorithm Using a Modified Crowding Distance
Abdesslem Layeb · Acta Informatica Pragensia · 2023
This paper presents two novel deterministic initialization procedures for k-means clustering based on a modified crowding distance.The procedures, named ck-means and fck-means, use more crowded points as initial centroids.Experimental studies on multiple datasets demonstrate that the proposed approach outperforms k-means and k-means++ in terms of clustering accuracy.The effectiveness of ck-means and fck-means is attributed to their ability to select better initial centroids based on the modified crowding distance.Overall, the proposed approach provides a promising alternative for improving k-means clustering.