A Vectorized Implementation for Maximum Entropy Based Associative Regression

We propose a supervised learning technique for associative regression. Assuming frequent patterns quantify correlations in dataset, we constrain the Sequential Conditional Generalized Iterative Scaling (SCGIS) convergence algorithm for Maximum Entropy (ME) models. We also assume prior probabilities on the ME model as a control on the step size in SCGIS. We have used the combinations of ME parameters and SCGIS probabilities as discriminative weights to frequent patterns. The weighted frequent patterns then predict the associative regression values. Experiments have been conducted on sparse numeric datasets, to find the regression error of our proposal. Our technique is comparable to the standard regression algorithms. As a concept, the proposed associative regression is useful as a parametric model in class association rule mining.