Cluster Analysis Online
Group cases into clusters of similar profiles, such as customer segments. Choose k-means or Ward’s hierarchical method and use the elbow plot to pick the number of clusters.
Updated · By Bibin XavierData 120 × 6
| Plottext · 120 levels | Cultivartext · 3 levels | Leaf_Lengthnumeric | Leaf_Widthnumeric | Stem_Heightnumeric | Yield_kgnumeric |
|---|---|---|---|---|---|
| L001 | Amber | 5.34 | 3.25 | 18.4 | 1.19 |
| L002 | Amber | 4.66 | 3.59 | 13.9 | 1.15 |
| L003 | Amber | 5.14 | 3.39 | 16 | 1.4 |
| L004 | Amber | 4.73 | 3.63 | 14.4 | 1.13 |
| L005 | Amber | 5.14 | 2.59 | 8.5 | 1.18 |
| L006 | Amber | 4.82 | 3.95 | 15.9 | 1.23 |
| L007 | Amber | 4.91 | 3.23 | 15.1 | 1.19 |
| L008 | Amber | 5.73 | 3.74 | 16.5 | 1.01 |
Variables and options
Results
120 rows · example dataFinal cluster centers (original units)
| Variable | Cluster 1 (n = 56) | Cluster 2 (n = 41) | Cluster 3 (n = 23) | F | p |
|---|---|---|---|---|---|
| Leaf_Length | 6.44 | 5.04 | 5.74 | 173.45 | < .001 |
| Leaf_Width | 2.92 | 3.42 | 2.57 | 95.01 | < .001 |
| Stem_Height | 20.80 | 14.37 | 16.48 | 111.71 | < .001 |
| Yield_kg | 1.90 | 1.23 | 1.41 | 100.94 | < .001 |
Note. The F tests describe which variables differ most between clusters. They are descriptive only, because the clusters were formed to maximize these differences.
Elbow plot
Clusters on the first two principal components
Interpretation
3 clusters account for 66.4% of the total variation. Read the cluster centers to name each segment by its distinctive profile. Try neighbouring values of k and pick the one after which the elbow plot flattens.
How to cite this tool
Using these results in a thesis, journal article, report or class assignment? Please cite this free tool so others can find it. It keeps the site free.
APA 7
Xavier, B. (2026). Cluster Analysis Online [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/cluster-analysis/
BibTeX
@software{xavier_clusteranalysis_2026,
author = {Xavier, Bibin},
title = {Cluster Analysis Online},
publisher = {FreeStatisticalTools.com},
year = {2026},
url = {https://freestatisticaltools.com/cluster-analysis/},
note = {Web application}
}Suggested sentence for your methods section: “The analysis was carried out with the free Cluster Analysis Online tool on FreeStatisticalTools.com (Xavier, 2026).”
What is cluster analysis?
Cluster analysis groups cases with similar profiles into clusters, for example customer or patient segments. Choose k-means or Ward’s hierarchical method, and use the elbow plot and silhouette score to pick the number of clusters.
When to use cluster analysis
Use it to find natural groupings, such as customer segments or patient profiles, when no group labels exist.
Assumptions
- Standardize variables measured on different scales.
- K-means looks for compact, similarly sized clusters; Ward’s method gives a tree you can cut at any level.
How to use this tool
- Enter your data. Paste columns from Excel, Google Sheets or SPSS with variable names in the first row, or upload a CSV or Excel file. The example data shows the expected layout.
- Choose the variables. Select the clustering variables.
- Set the options. Fill in number of clusters, method and standardize variables (z-scores).
- Read the results. Results update automatically. Read the plain-English interpretation, then use Copy table to paste APA-style tables into Word and Copy for the results sentence.
Frequently asked questions
K-means or hierarchical clustering?
K-means is fast and suits larger samples. Ward’s hierarchical method gives a dendrogram that shows how clusters merge, useful for choosing k with small samples.
Should I standardize variables?
Yes, when variables are measured on different scales; otherwise variables with large ranges dominate the distances.
How do I choose the number of clusters?
Look for the elbow where the within-cluster sum of squares stops falling quickly, and prefer solutions with a higher silhouette score that make substantive sense.
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