Associative Classification of Mammograms using Weighted Rules

In this paper, we present a novel method for the classification of mammograms using a unique weighted association rule based classifier. Images are preprocessed to reveal regions of interest. Texture components are extracted from segmented parts of the image and discretized for rule discovery. Association rules are derived between various texture components extracted from segments of images, and employed for classification based on their intra- and inter-class dependencies. These rules are then employed for the classification of a commonly used mammography dataset, and rigorous experimentation is performed to evaluate the rules' efficacy under different classification scenarios. The experimental results show that this method works well for such datasets, incurring accuracies as high as 89%, which surpasses the accuracy rates of other rule based classification techniques.