Hiva Allahyari , pp. 40. COM/School of Computing, 2010.
This paper reviews methods for evaluating and analyzing the comprehensibility and understandability of models generated from data in the context of data mining and knowledge discovery. The motivation for this study is the fact that the majority of previous work has focused on increasing the accuracy of models, ignoring user-oriented properties such as comprehensibility and understandability. Approaches for analyzing the understandability of data mining models have been discussed on two different levels: one is regarding the type of the models’ presentation and the other is considering the structure of the models. In this study, we present a summary of existing assumptions regarding both approaches followed by an empirical work to examine the understandability from the user’s point of view through a survey. From the results of the survey, we obtain that models represented as decision trees are more understandable than models represented as decision rules. Using the survey results regarding understandability of a number of models in conjunction with quantitative measurements of the complexity of the models, we are able to establish correlation between complexity and understandability of the models.