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» Improving Mining Quality by Exploiting Data Dependency
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UAI
1996
15 years 1 months ago
Learning Bayesian Networks with Local Structure
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly ...
Nir Friedman, Moisés Goldszmidt
PKDD
2010
Springer
153views Data Mining» more  PKDD 2010»
14 years 9 months ago
A Robustness Measure of Association Rules
Abstract. We propose a formal definition of the robustness of association rules for interestingness measures. It is a central concept in the evaluation of the rules and has only be...
Yannick Le Bras, Patrick Meyer, Philippe Lenca, St...
KDD
2009
ACM
192views Data Mining» more  KDD 2009»
16 years 11 days ago
Learning optimal ranking with tensor factorization for tag recommendation
Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or de...
Steffen Rendle, Leandro Balby Marinho, Alexandros ...
KDD
2007
ACM
179views Data Mining» more  KDD 2007»
15 years 6 months ago
Mining statistically important equivalence classes and delta-discriminative emerging patterns
The support-confidence framework is the most common measure used in itemset mining algorithms, for its antimonotonicity that effectively simplifies the search lattice. This com...
Jinyan Li, Guimei Liu, Limsoon Wong
ICDM
2003
IEEE
158views Data Mining» more  ICDM 2003»
15 years 5 months ago
Combining Multiple Weak Clusterings
A data set can be clustered in many ways depending on the clustering algorithm employed, parameter settings used and other factors. Can multiple clusterings be combined so that th...
Alexander P. Topchy, Anil K. Jain, William F. Punc...