PROBABILIST, POSSIBILIST AND BELIEF OBJECTS FOR PATTERN-RECOGNITION BY DATA-ANALYSIS

The main aim of the symbolic approach in data analysis is to extend problems, methods and algorithms used on classical data to more complex data called ''symbolic objects'' which are well adapted to representing knowledge and which ''unify'' unlike usual observations which characterize ''individual things''. We introduce several kinds of symbolic objects : boolean, possibilist, probabilist and belief. We briefly present some of their qualities and properties; three theorems show how Probability, Possibility and Evidence theories may be extended on these objects. Finally four kinds of data analysis problems including the symbolic extension are presented.