Multi-dimensional Information Ordering to Support Decision-Making Processes

Abstract

Massive amounts of textual and digital data are created daily from business or public activities. The organisation, mining and summarization of such a rich and large information source is required to capture the essential and critical knowledge it contains. Such a mining is of strategic importance in many domains including innovation (eg to mine technological reviews and scientific literature) and electronic commerce (eg to mine customer reviews). Information content generally bears several important aspects, mapped onto visualisation dimensions, whose number needs to be reduced to enable relevant interactive exploration. In this paper, we propose a novel strategy to mine and organise document sets, in order to present them in a consistent manner and to highlight interesting and relevant information patterns they contain. We base our method on the formulation of a global optimisation problem solved by using the Traveling Salesman Problem (TSP) approach. We show how this compact formulation opens interesting possibilities for the mining of document collections mapped onto multidimensional information sets. We discuss the issue of scalability and show that associated scalable solutions exist. We demonstrate the effectiveness of our method over several types of documents, embedded into real business cases.