Editing Unsupervised Clustering (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a production segmentation system:
# Feature engineering: create behavioral, transactional, and demographic features.
# Preprocessing: standardize, handle missing values, remove collinear features.
# Dimensionality reduction: PCA to 10-20 components retaining 90% variance.
# Cluster search: evaluate K-means for K=2–15 using silhouette score and domain knowledge.
# Stability check: run clustering 10× with different random seeds; stable segments persist.
# Interpretation: characterize each cluster by mean feature values and example members; name segments (e.g., "High-value champions," "At-risk churners").
# Deployment: assign new users to nearest centroid in real time for personalization.

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:Unsupervised Learning]]
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