Visualization support to better comprehend and improve decision tree classification modelling process: a survey and appraisal

Abstract

Data mining (DM) can be defined as the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data. Modelling is the crucial step where DM algorithms are applied in order to extract data patterns. In order for domain experts, who play significant roles in DM process, to make the most efficient and effective use of DM tools, these tools must incorporate appropriate visualization to facilitate the process of modelling. Yet, unfortunately, study of how visualization should be designed, particularly what components should be included and how to present them, has been rather limited. This paper surveys the current state of art in application of visualization techniques to better comprehend and improve the decision trees modelling process in three modes: visualization of tree models, visualization of model evaluation and visual interactive tree construction. A number of issues that have been overlooked and areas that need to be improved are identified through reviewing a collection of related research and examining six current DM softwares in terms of their design of a few important features in each mode of the visualization support to decision trees classification modelling. Although this article focuses on decision trees classification modelling, guidelines derived from this study can be beneficial to other modelling techniques as well. At the end of the paper, a desirable design of visualization support to DM modelling is proposed with a conceptual model.

Keywords:

• Data mining
• Visualization
• Decision tree
• Classification
• Human–computer interaction.