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name: frequent-patterns description: "Mine frequent itemsets and association rules from transaction data with mlxtend frequent_patterns." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Frequent Patterns

Use this sub-skill when the task involves market-basket or transaction mining with mlxtend.frequent_patterns: frequent itemsets, maximal itemsets, association rules, support/confidence/lift-style metrics, null-aware FP mining, sparse one-hot data, or threshold diagnosis.

Route first

• Start with references/data-formats.md when the input is raw transactions, one-hot pandas data, sparse data, nullable data, or previously mined itemsets/rules.
• Use references/api-reference.md for verified function signatures, return columns, algorithm differences, and metric names.
• Use references/workflows.md for transaction-to-rules recipes, algorithm selection, rule filtering, null handling, and support/threshold tuning.
• Use references/troubleshooting.md for invalid inputs, missing columns, empty results, metric errors, nullable/sparse warnings, and memory growth.
• Run scripts/frequent_patterns_smoke.py to verify that the installed package can encode transactions, mine itemsets, and generate rules on a tiny deterministic dataset.

Boundaries

• Prefer TransactionEncoder only as the upstream route from list-of-lists transactions into a one-hot pandas DataFrame. For broader preprocessing and transformer behavior, route to ../feature-workflows/SKILL.md.
• Do not handle plotting or visualization here. Route rule plots, heatmaps, and Matplotlib utilities to ../plotting-and-utilities/SKILL.md.
• Keep downstream mining code package-based; the runtime workflow must use only installed package APIs and bundled skill files.

Fast decision map

Goal	Primary API	Notes
All frequent itemsets, classic candidate search	apriori	Supports low_memory and n_jobs; no nullable-NaN mode.
All frequent itemsets, usually faster on sparse/large baskets	fpgrowth	FP-tree based; supports null_values=True.
Only maximal frequent itemsets	fpmax	Compact output; not a full subset lattice for normal rule metrics.
All frequent itemsets via H-Mine	hmine	Alternative frequent-itemset miner; no nullable-NaN mode.
Rules from complete frequent itemsets	association_rules	Requires support and itemsets; use full itemset output unless support_only=True.
Raw transactions to one-hot DataFrame	TransactionEncoder	Create pd.DataFrame(te.fit_transform(transactions), columns=te.columns_).