| Document Date: 2006-05-03 17:58:57
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City Bern / / Company IBM / / Currency pence / / / Facility Zoo Data Set We / mine ETIs / Zoo Database / Chapel Hill / square AFI / Operations Research University of North Carolina / mine AFIs / / IndustryTerm experimental technology / association rule mining / data mining algorithm / frequent pattern mining fails / noise tolerant algorithms / frequent itemset mining algorithm / classical frequent itemset algorithms / itemset mining / frequent pattern mining algorithms / frequent itemset algorithms / noise-tolerant itemset mining / heuristic algorithm / noise-tolerant frequent itemset mining / error-tolerant algorithms / frequent pattern mining / frequent itemset mining methods / frequent itemset mining / frequent itemset mining approaches / breadth-first frequent itemset mining / classical frequent itemset mining / mining / wise mining / real data applications / search space / exhaustive level-wise algorithm / / Organization Department of Statistics / Department of Computer Science / University of North Carolina / Chapel Hill / / Person Yang et.al / / / ProgrammingLanguage C / / ProvinceOrState North Carolina / / Technology frequent itemset algorithms / heuristic algorithm / AFI mining algorithm / error-tolerant algorithms / noise tolerant algorithms / present / classical frequent itemset algorithms / underlying experimental technology / machine learning / 4.1 Mining AFIs The algorithm / AFI algorithm / frequent pattern mining algorithms / DNA Chip / gene expression / AFI The AFI algorithm / exhaustive level-wise algorithm / frequent itemset mining algorithm / simulation / data mining algorithm / 1 4 AFI Mining Algorithm / /
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Mining Approximate Frequent Itemsets In the Presence of Noise: Algorithm and Analysis 1 Jinze Liu, 1 Susan Paulsen, 2 Xing Sun, 1 Wei Wang, 1,2 Andrew Nobel, 1 Jan Prins 1