A Competitive Intelligence Solution to Predict Competitor Action Using K-modes Algorithm and Rough Set Theory

Abstract

We will focus in this paper on the competitive intelligence problem which deals with the competitive environment of a company. Our purpose is to predict and anticipate the action of its competitor. We are talking here about a context of reasoning under uncertainty. All existed works define the concept of competitive intelligence and propose a scheme for the competitive intelligence process and its stages, but there is no work, at the best of our knowledge, that touched the practical aspect of the field or developed a complete competitive intelligence solution that can be delivered to the decision maker, which makes the originality of our work. To motivate the research, we will address a competitive practical case in the field of telecommunications. In this paper we propose a competitive intelligence solution composed by two steps: actions association using k-modes algorithm which has the capability to deal with nominal data, and actions generation using rough set theory which has the capability to deal with inexact data and drive rules from it.