A context-aware data mining process model based framework for supporting evaluation of data mining results

Abstract

The knowledge discovery via data mining process (KDDM) is a multiple phase that aims to at a minimum semi-automatically extract new knowledge from existing datasets. For many data mining tasks, the evaluation phase is a challenging one for various reasons. Given this challenge several studies have presented techniques that could be used for the semi-automated evaluation of data mining results. When taken together, these studies suggest the possibility of a common multi-criteria evaluation framework. The use of such a multi-criteria evaluation framework, however, requires that relevant objectives, measures and preference function be identified. This implies that the context of the DM problem is particularly important for the evaluation phase of the KDDM process. Our framework utilizes and integrates a pair of established tightly coupled techniques (i.e. Value Focused Thinking (VFT) and the Goal–Question–Metric (GQM) methods) as well as established techniques from multi-criteria decision analysis in order to explicate and utilize context information in order to facilitate semi-automated evaluation.