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SAC
2010
ACM
13 years 3 months ago
A study on interestingness measures for associative classifiers
Associative classification is a rule-based approach to classify data relying on association rule mining by discovering associations between a set of features and a class label. Su...
Mojdeh Jalali Heravi, Osmar R. Zaïane
WSDM
2012
ACM
259views Data Mining» more  WSDM 2012»
12 years 4 months ago
Learning recommender systems with adaptive regularization
Many factorization models like matrix or tensor factorization have been proposed for the important application of recommender systems. The success of such factorization models dep...
Steffen Rendle
NN
2008
Springer
152views Neural Networks» more  NN 2008»
13 years 8 months ago
Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge
We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in ...
Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin C...
KDD
2002
ACM
106views Data Mining» more  KDD 2002»
14 years 9 months ago
Selecting the right interestingness measure for association patterns
Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as...
Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava
FUIN
2006
107views more  FUIN 2006»
13 years 8 months ago
Learning Sunspot Classification
Sunspots are the subject of interest to many astronomers and solar physicists. Sunspot observation, analysis and classification form an important part of furthering the knowledge a...
Trung Thanh Nguyen, Claire P. Willis, Derek J. Pad...