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KDD
2001
ACM
196views Data Mining» more  KDD 2001»
16 years 7 months ago
Efficient discovery of error-tolerant frequent itemsets in high dimensions
We present a generalization of frequent itemsets allowing the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifie...
Cheng Yang, Usama M. Fayyad, Paul S. Bradley
ICDM
2002
IEEE
156views Data Mining» more  ICDM 2002»
16 years 4 days ago
Association Analysis with One Scan of Databases
Mining frequent patterns with an FP-tree avoids costly candidate generation and repeatedly occurrence frequency checking against the support threshold. It therefore achieves bette...
Hao Huang, Xindong Wu, Richard Relue
SDM
2007
SIAM
137views Data Mining» more  SDM 2007»
15 years 8 months ago
Semi-supervised Feature Selection via Spectral Analysis
Feature selection is an important task in effective data mining. A new challenge to feature selection is the so-called “small labeled-sample problem” in which labeled data is...
Zheng Zhao, Huan Liu
KDD
2006
ACM
130views Data Mining» more  KDD 2006»
16 years 7 months ago
Discovering significant rules
In many applications, association rules will only be interesting if they represent non-trivial correlations between all constituent items. Numerous techniques have been developed ...
Geoffrey I. Webb
KDD
2004
ACM
148views Data Mining» more  KDD 2004»
16 years 7 months ago
Interestingness of frequent itemsets using Bayesian networks as background knowledge
The paper presents a method for pruning frequent itemsets based on background knowledge represented by a Bayesian network. The interestingness of an itemset is defined as the abso...
Szymon Jaroszewicz, Dan A. Simovici