A Novel Association Rule Prediction Algorithm for Classification and Regression

To avoid generating a large number of candidate itemsets during the association rules mining and improve the prediction performance, a new association rule prediction algorithm for classification and regression (ARPACR) is proposed according to the advantages of matrix operation and tree structure. Firstly, the association rules are mined by constructing a new frequent tree. Then, the consequents of the association rules are reconstructed to achieve the classification and regression prediction for new sample. Finally, the experiment results show that it is competitive in prediction accuracy and mining efficiency by comparing with the other algorithms.