Interactive Visual Decision Tree Classification

Abstract

Data mining (DM) modeling is a process of transforming information enfolded in a dataset into a form amenable to human cognition. Most current DM tools only support automatic modeling, during which uses have little interaction with computing machines other than assigning some parameter values at the beginning of the process. Arbitrary selection of parameter values, however, can lead to an unproductive modeling process. Automatic modeling also downplays the key roles played by humans in current knowledge discovery systems. Classification is the process of finding models that distinguish data classes in order to predict the class of objects whose class labels are unknown. Decision tree is one of the most widely used classification tools. A novel interactive visual decision tree (IVDT) classification process has been proposed in this research; it aims to facilitate decision tree classification process regarding enhancing users’ understanding and improving the effectiveness of the process by combining the flexibility, creativity, and general knowledge of humans with the enormous storage capacity and computational power of computers. An IVDT for categorical input attributes has been developed and experimented on twenty subjects to test three hypotheses regarding its potential advantages. The experimental results suggested that compared to the automatic modeling process as typically applied in current decision tree modeling tools, IVDT process can improve the effectiveness of modeling in terms of producing trees with relatively high classification accuracies and small sizes, enhance users’ understanding of the algorithm, and give them greater satisfaction with the task.