Enhancing business decision making through actionable knowledge discovery using an hybridized MCDM model

Abstract

In recent years, with the increase in the usage of internet-enabled electronic devices and information systems, the upsurge and availability of volumes of high dimensional data have become one of the sources of high business value. The need for businesses to make informed decisions by leveraging on the patterns from the multi-dimensional data have become paramount. However, the major issue is whether or not the patterns can optimize business decision making process to increase profit. Hence, there is need for actionable knowledge discovery (AKD). Therefore, this paper proposed an hybridized interval type-2 fuzzy Multi Criteria Decision Making (MCDM) model for evaluating patterns based on three subjective interestingness measure which are unexpectedness, actionability and novelty. The interval type-2 Fuzzy Analytical Hierarchy Process (AHP) was employed to weigh the patterns and Compensatory AND approach was utilized for ranking the patterns using the three subjective interestingness measures. The proposed model depicts its applicability in identifying and ranking the patterns which are more relevant for enhancing business decision making.