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Analyze another fileApplies K-means clustering to HRIS employee data to discover natural workforce segments. Uses age, income, satisfaction, tenure, and performance to identify distinct employee groups. Produces cluster profiles, PCA scatter plots, and actionable retention strategies for each segment.
Use this when you need workforce segmentation via k-means clustering on your data.
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Built for: Analyst, data scientist, business user
Typical data source: CSV with relevant columns
Applies K-means clustering to HRIS employee data to discover natural workforce segments. Uses age, income, satisfaction, tenure, and performance to identify distinct employee groups. Produces cluster profiles, PCA scatter plots, and actionable retention strategies for each segment.
Number of employees in each cluster
Average feature values per cluster
Employees projected onto first two principal components, colored by cluster
Standardized feature means per cluster
Optimal cluster count selection via silhouette scores
Cluster distribution across departments
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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