A Novel Visualization Approach for Data-Mining-Related Classification

Classification and categorization are common tasks in data mining and knowledge discovery. Visualizations of classification models can create understanding and trust in data mining models. However, existing visualizations are often complex or restricted to specific classifiers and attributes. In this work, we propose an intuitive visualization system to observe and understand classification processes and results. Our system can handle multiple classes, nominal and numeric attributes, and supports all classifiers whose predictions can be interpreted as probabilities. We state that the possibility to observe the training process of a classifier boosts the understanding of classification results also for non-expert users. In combination with an intuitive visualization, we provide a system to generate in-depth understanding of classification processes and results. Our simulations revealed that the system could support the user to better understand a classifier's decision, and to gain insights into classification processes.