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» Efficient Discovery of Confounders in Large Data Sets
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KDD
1997
ACM
135views Data Mining» more  KDD 1997»
15 years 10 months ago
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 c...
Roberto J. Bayardo Jr.
CAV
2005
Springer
104views Hardware» more  CAV 2005»
15 years 12 months ago
Expand, Enlarge and Check... Made Efficient
Abstract. The coverability problem is decidable for the class of wellstructured transition systems. Until recently, the only known algorithm to solve this problem was based on symb...
Gilles Geeraerts, Jean-François Raskin, Lau...
ICDM
2010
IEEE
228views Data Mining» more  ICDM 2010»
15 years 4 months ago
Multi-label Feature Selection for Graph Classification
Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug acti...
Xiangnan Kong, Philip S. Yu
CVPR
2009
IEEE
17 years 1 months ago
Regularized Multi-Class Semi-Supervised Boosting
Many semi-supervised learning algorithms only deal with binary classification. Their extension to the multi-class problem is usually obtained by repeatedly solving a set of bina...
Amir Saffari, Christian Leistner, Horst Bischof
ICPR
2008
IEEE
16 years 23 days ago
Incremental clustering via nonnegative matrix factorization
Nonnegative matrix factorization (NMF) has been shown to be an efficient clustering tool. However, NMF`s batch nature necessitates recomputation of whole basis set for new samples...
Serhat Selcuk Bucak, Bilge Günsel