Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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This paper tackles the problem of knowledge discovery in text collections and the dynamic display of the discovered knowledge. We claim that these two problems are deeply interleaved, and should be considered together. The contribution of this paper is fourfold : (1) description of the properties needed for a high level representation of concept relations in text (2) a stochastic measure for a fast evaluation of dependencies between concepts (3) a visualization algorithm to display dynamic structures and (4) a deep integration of discovery and knowledge visualization, i.e. the placement of nodes and edges automatically guides the discovery of knowledge to be displayed. The resulting program has been tested using two specific data sets based on the specific domains of molecular biologyand WWW howtos.