Association rule mining — Apriori & FP-Growth.
mediumAnswer
- Find frequent itemsets in transactions (e.g. 'diapers → beer').
- Apriori: level-wise search, prune non-frequent supersets — slow, many DB scans.
- FP-Growth: build FP-tree in one scan, mine recursively — much faster.
- Metrics: support (frequency), confidence , lift (association strength vs random).
- Classic market-basket analysis; modern replaced by embedding-based recsys but still asked in interviews.
Check yourself — multiple choice
- Random
- Find frequent itemsets: Apriori (level-wise, slow) vs FP-Growth (FP-tree in one scan, fast); support / confidence / lift; classic market-basket, still interview-relevant
- Same as k-means
- Not real
Association rules: Apriori vs FP-Growth; support / confidence / lift.
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Practise Unsupervised Learning
214 interview questions in this topic.