The k-means algorithm: A comprehensive survey and performance evaluation

Mohiuddin Ahmed, Raihan Seraj, Syed Mohammed Shamsul Islam

Research output: Contribution to journalReview articlepeer-review

476 Citations (Scopus)


The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.

Original languageEnglish
Article number1295
Pages (from-to)1-12
Number of pages12
JournalElectronics (Switzerland)
Issue number8
Publication statusPublished - Aug 2020


Dive into the research topics of 'The k-means algorithm: A comprehensive survey and performance evaluation'. Together they form a unique fingerprint.

Cite this