K-Means Clustering
Also known as: K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.
Key highlights
- Simple, fast, and scalable to large datasets with low mathematical overhead.
- Produces clear, interpretable cluster assignments and centroids for segmentation work.
- Assumption-light on distributions: it does not require normally distributed data.
- Widely accepted across business, marketing, and exploratory data analysis.
Intuition
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How it works
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When to use it
Use K-Means for exploratory analysis or classification of continuous and count-type features when you want to partition observations into a pre-specified number of groups and have at least about 50 observations. It assumes roughly spherical, similarly sized clusters and requires features to be standardised beforehand. When the sample is small (n below about 50) or the spherical-cluster assumption is doubtful, hierarchical clustering is the safer, more exploratory alternative.
Strengths & limitations
- Simple, fast, and scalable to large datasets with low mathematical overhead.
- Produces clear, interpretable cluster assignments and centroids for segmentation work.
- Assumption-light on distributions: it does not require normally distributed data.
- Widely accepted across business, marketing, and exploratory data analysis.
- The number of clusters k must be fixed in advance rather than discovered from the data.
- It assumes spherical, comparably sized clusters and struggles with irregular shapes.
- Results depend on standardisation and on the random initial centroids.
- On small samples (n below about 50) the centroids are unstable, giving inconsistent results across different starting points.
Common pitfalls
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Applications
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Frequently asked
How do I choose the number of clusters k?
k must be set before running the algorithm. The elbow method (plotting within-cluster variance against k) and silhouette analysis are the standard ways to choose and validate a sensible value.
Do I need to scale my data first?
Yes. Standardisation is mandatory because K-Means relies on distance: without it, a feature with a large range will dominate the cluster assignments and distort the grouping.
Why do I get different clusters each time?
K-Means starts from random initial centroids, so results can vary, especially on small samples where the centroids are unstable. Running multiple initialisations and keeping the best, or using a more exploratory method like hierarchical clustering, helps.
What should I check before and after clustering?
Beforehand, confirm the data is clusterable (for example with a Hopkins statistic of at least about 0.75) and choose k via the elbow method and silhouette analysis. Afterwards, report the silhouette score, describe each cluster's profile, and visualise the clusters with PCA or t-SNE.
Sources
- 1.MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
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ScholarGate. (2026, June 1). K-Means Clustering. ScholarGate. https://scholargate.app/machine-learning/k-means-clustering