Actionable Knowledge Discovery for Increasing Enterprise Profit, Using Domain Driven-Data Mining

Abstract

Actionable Knowledge Discovery approaches to extract the business and technical significant actions/patterns to support direct decision making. These actions suggest how to transform an object from an undesirable status to a desirable status by incurring less cost and high profit. This article aims to propose a work that generates actionable patterns efficiently. It reduces the search space and number of iterations for attribute value change during action generation. Performance of the proposed method is compared with Yang’s method and OF-CEAMA on the basis of four parameters i.e. the total number of rules required for action generation, run time of the methods, the total number of generated actions, total net profit and time and space complexity. Experiments have been carried out on four datasets retrieved from UCI Machine learning repository. Experimental results show that the proposed work takes less time than the other two methods to extract actions for all datasets. Also, the number of rules required to generate actions are less than the other two methods. Results also suggest that a decrease in execution time does not compromise the information and proposed work generates the same actions and net profit. Moreover, the proposed work tries to transfer an object from undesired status to the desired status.