We describe a P2P association rule mining descriptor enrichment approach that statistically significantly increases accuracy by greater than 15% over the non-enriched baseline. Unl...
Nazli Goharian, Ophir Frieder, Wai Gen Yee, Jay Mu...
As the size and dimensionality of data sets increase, the task of feature selection has become increasingly important. In this paper we demonstrate how association rules can be us...
In recent years, the weakness of the canonical support-confidence framework for associations mining has been widely studied. One of the difficulties in applying association rules ...
We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transact...
Alexandre V. Evfimievski, Ramakrishnan Srikant, Ra...
In traditional classification setting, training data are represented as a single table, where each row corresponds to an example and each column to a predictor variable or the targ...