⚡ Bolt: Cache Apriori matches and optimize pruning in compliance miner

[n24q02m]

💡 What:

• Implemented DP caching (pattern_indices) in Apriori candidate generation for prefix patterns to avoid repeated O(N) set intersections.
• Refactored the any() generator expression in most-general-first pruning to use an explicit early-exit loop.
• Grouped kept patterns by target to limit subset checking bounds.

🎯 Why:
The compliance miner matches() method previously re-calculated the complete intersection for every Apriori pattern. Additionally, using generator expressions inside any() during the pruning stage added significant Python function-call and memory-allocation overhead on an extreme hot path (looping over all candidate/kept combinations).

📊 Impact:
Reduces compliance mining duration by eliminating redundant O(N) operations and heavy allocation overhead. On synthetic data (1000 items), execution time improved from ~0.60s to ~0.37s.

🔬 Measurement:
Run the tests locally (uv run pytest tests/test_compliance_miner.py) or create a benchmarking script wrapping mine_compliance_rules with python's time module.

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PR created automatically by Jules for task 972599366241330016 started by @n24q02m

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