In recent years, there have been increasing efforts in applying association rule mining to build Associative Classification (AC) models. However, the similar area that applies association rule mining to build Associative Regression (AR) models has not been well explored. In this work, we fill this gap by presenting a novel regression model based on association rules called AREM. AREM starts with finding a set of regression rules by applying the instance based pruning strategy, in which the best rules for each instance are discovered and combined. Then a probabilistic model is trained by applying the EM algorithm, in which the right hand side of the rules and their importance weights are updated. The extensive experimental evaluation shows that our model can perform better than both the previously proposed AR model and some of the state of the art regression models, including Boosted Regression Trees, SVR, CART and Cubist, with the Mean Squared Error (MSE) being used as the performance metric.
Jiang, Zhonghua; Karypis, George.
AREM: A Novel Associative Regression Model Based on EM Algorithm.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.