ck-means and fck-means: Two Deterministic Initialization Procedures for k-means Algorithm Using a Modified Crowding Distance

Abstract

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.

Keywords

• clustering
• kmeans
• kmeans++
• initialization
• crowding distance
• heuristics