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.
|Original language||English (US)|
|Title of host publication||Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings|
|Number of pages||12|
|State||Published - 2013|
|Event||17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia|
Duration: Apr 14 2013 → Apr 17 2013
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013|
|City||Gold Coast, QLD|
|Period||4/14/13 → 4/17/13|
Copyright 2014 Elsevier B.V., All rights reserved.
- Association rule
- Associative regression
- EM algorithm
- Instance based pruning.
- Probabilistic model
- Regression rule