MEDLINE is the largest biomedical literature database. It is updated daily with 200-4,000 citations. This permanent growth induces the need of a good MEDLINE abstract clustering to accelerate the procedure of research and information retrieval. Several works have been developed in this context, but clustering MEDLINE abstracts are still an area where researchers are trying to propose new approaches to better clustering. Over the last few years, evolutionary algorithms have been widely applied to clustering problems because of their ability to avoid local optimal solutions and converge to a global one. In this article, a new approach is proposed for clustering MEDLINE abstracts based on an extension of an evolutionary algorithm which is the genetic algorithm combined with a Vector Space Model and an agglomerative algorithm.
Competitive intelligence deals with the competitive environment of a company. Several studies have been conducted on competitive intelligence domain but there is no empirical work that gives a complete implemented competitive intelligence solution. This paper presents an overview of competitive intelligence studies and highlights the issues towards developing a complete CI solution. A new conceptual model which details the collection phase and incorporates the anticipation of the competitor decisions is proposed. To better solve the CI problem, the paper proposes to integrate the ability of multi-criteria decision aid methods to manage conflicting criteria in a complex environment, with the ability of artificial intelligence in managing and extracting large amount of technical data/information in such a context.
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