clustering-and-segmentation
Segment populations using clustering and related methods — with validation, interpretability, and actionable segment definitions. Use when discovering customer/user segments or grouping entities for differentiated treatment.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
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01 前言 Front Matter
- name
- clustering-and-segmentation
- description
- Segment populations using clustering and related methods — with validation, interpretability, and actionable segment definitions. Use when discovering customer/user segments or grouping entities for differentiated treatment.
03 SKILL.md 正文
Clustering and Segmentation
Overview
Segmentation groups similar entities so strategy can differentiate. Clusters must be stable enough and meaningful enough to act on — not only algorithmically neat.
When to Use
- Customer or user segmentation
- Behavioral clustering for product or marketing
- Grouping entities for operational treatment
- Exploring structure in high-dimensional data
Core Practices
- Select features aligned to the segmentation purpose
- Scale and preprocess appropriately
- Try multiple algorithms/k and validate (stability, separation, domain sense)
- Profile segments in human-readable terms
- Test whether segments predict different outcomes or responses
- Name and document segments for operational use
Principles
- Segments exist to change decisions
- Unstable clusters are dangerous to operationalize
- More segments are not always better
- Domain review is part of validation
Verification
- [ ] Purpose of segmentation is explicit
- [ ] Segments are interpretable and reasonably stable
- [ ] Action implications are defined
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