User behavior segmentation — feature engineering.

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Answer

$(1) RFM: Recency (days since last activity), Frequency (activities per period), Monetary (spend).$
$(2) Behavioral counts: events per day/week.$
$(3) Categorical: preferred category, region, device.$
$(4) Sequences: transformer / GRU embed of event sequence.$
$(5) Time-decay weighted (recent behavior matters more).$
$(6) Log-transform skewed monetary.$
$Then z-score, PCA/UMAP \to k-means or HDBSCAN.$
$Standard for marketing / product analytics segments.$

Check yourself — multiple choice

Just raw features
RFM (Recency / Frequency / Monetary) + behavioral counts + categoricals + sequence embed + time-decay + log-transform skewed → z-score + PCA → k-means/HDBSCAN
Random
Not real

User segmentation: RFM + behavior + sequence embed + log-transform + cluster.

#applications#clustering

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