Clustering Analysis Plan
Group similar records and judge the result.
The Open buttons send the prompt text as currently shown below to that site only when you click. Fill in the placeholders first.
Plan a clustering analysis. Records and features: [RECORDS_AND_FEATURES]. Why I want groups: [PURPOSE]. Data size and issues: [DATA_NOTES]. Produce: 1. Preparation: choose features that matter for the purpose, scale numeric features, handle categorical variables (for example Gower distance or one-hot with care), and reduce dimensions if needed. 2. Method comparison table: k-means, hierarchical, DBSCAN, Gaussian mixtures: what each assumes, strengths, weaknesses, and fit to my data. 3. Choosing the number of clusters: elbow plot, silhouette score, stability across random seeds and subsamples; explain that there may be no single right number. 4. Profiling: a table per cluster with size and average of each feature versus overall, plus a plain-language name. 5. Validation: do the clusters differ on variables not used to form them? Are they stable? Would a business user act differently on each? 6. Code outline. 7. Cautions: clusters are a way of describing data, not natural kinds; avoid naming clusters in ways that stereotype people. End with a decision: use, refine, or drop, with criteria.
Fill in before use
Optional: type your details and the prompt above updates. Nothing is saved or sent.
Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.