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KDD
1997
ACM

Brute-Force Mining of High-Confidence Classification Rules

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Brute-Force Mining of High-Confidence Classification Rules
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.
Roberto J. Bayardo Jr.
Added 08 Aug 2010
Updated 08 Aug 2010
Type Conference
Year 1997
Where KDD
Authors Roberto J. Bayardo Jr.
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