Brute-Force Mining of High-Confidence Classification Rules

Roberto J. Bayarod Jr.

This paper investigates a brute-force technique for mining classification rules from large data sets. We employ an association rule miner enhanced with new pruning strategies to control combinatorial explosion in the number of candidates counted with each database pass. The approach effectively and efficiently extracts high confidence classification rules that apply to most if not all of the data in several classification benchmarks.

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