AREM: A Novel Associative Regression Model Based on the EM Algorithm

Zhonghua Jiang and George Karypis
17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2013
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Inrecentyears,therehavebeenincreasingeffortsinapplying 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 re- gression 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 up- dated. 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.
Research topics: Classification | Data mining