Data Mining with Possibilistic Graphical Models

Data Mining, also called Knowledge Discovery in Databases, is a young area of research, which has emerged in response to the flood of data we are faced with nowadays. It has taken up the challenge to develop techniques that can help humans discover useful patterns in their data. One such technique—which certainly is among the most important, as it can be used for frequent data mining tasks like classifier construction and dependence analysis—are graphical models and especially learning such models from a dataset of sample cases. In this paper we review the basic ideas of graphical modeling, with a focus on possibilistic networks, and study the principles of learning such graphical models from a dataset of sample cases.