ARC-UI: Visualization Tool for Associative Classifiers

The classification of an unknown item based on a training data set is a key data mining task. An important part of this process that is often overlooked is the user's comprehension of the classifier and the results it produces. Associative classifiers begin to address this issue by using sets of simple rules to classify items. However, the size of these rule sets can be an obstacle to understandability. In this work, we present an interactive visualization system that allows the user to visualize various aspects of the classifier's decision process. This system shows the rules that are relevant to the classification of an item, the ways in which the item's characteristics relate to these rules, and connections between the item and the classifier's training data set. The system also contains a speculation component, which allows the user to modify rules within the classifier, and see the impact of these changes. Thus, this component allows the user to contribute domain expertise to the classification process, consequently improving the accuracy of the classifier.