CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance
This work addresses the initialization bottleneck in K-means clustering for data analysis applications, but it is incremental as it builds on existing methods like Kmeans++.
The paper tackles the problem of initial centroid selection in K-means clustering by introducing two deterministic initialization procedures, CKmeans and FCKmeans, based on a modified crowding distance, which outperform Kmeans and Kmeans++ in clustering accuracy on multiple datasets.
This paper presents two novel deterministic initialization procedures for K-means clustering based on a modified crowding distance. The procedures, named CKmeans and FCKmeans, use more crowded points as initial centroids. Experimental studies on multiple datasets demonstrate that the proposed approach outperforms Kmeans and Kmeans++ in terms of clustering accuracy. The effectiveness of CKmeans and FCKmeans 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.