# CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance

arXiv: https://arxiv.org/abs/2304.09989 · HTML: https://mlsanity.com/paper/2304.09989/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)

## Built on by (most cited first, 1 of 1)

- [Sector-Mean: Deterministic Initialization of K-Means Centroids via Angular Sector Partitioning](https://mlsanity.com/paper/2609.06468/) (2026)

## Abstract (arXiv)

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.