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

Abdesslem Layeb

Acta Informatica Pragensia, 2023, vol. 2023, issue 2, 379-399

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 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://aip.vse.cz/doi/10.18267/j.aip.223.html (text/html)
http://aip.vse.cz/doi/10.18267/j.aip.223.pdf (application/pdf)
free of charge

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:prg:jnlaip:v:2023:y:2023:i:2:id:223:p:379-399

Ordering information: This journal article can be ordered from
Redakce Acta Informatica Pragensia, Katedra systémové analýzy, Vysoká škola ekonomická v Praze, nám. W. Churchilla 4, 130 67 Praha 3
http://aip.vse.cz

DOI: 10.18267/j.aip.223

Access Statistics for this article

Acta Informatica Pragensia is currently edited by Editorial Office

More articles in Acta Informatica Pragensia from Prague University of Economics and Business Contact information at EDIRC.
Bibliographic data for series maintained by Stanislav Vojir ().