Aspects of Uncertainty Handling for Knowledge Discovery in Databases
نویسندگان
چکیده
In this paper we discuss the role of uncertainty in Knowledge Discovery in Databases (KDD) and discuss the applicability of Evidence Theory towards achieving the goal of handling the uncertainty successfully, incorporating it into the discovery process. We claim that Evidence Theory is more suitable for representing and handling uncertainty within KDD than the Bayesian Model and present a case for the same. We discuss , EDM, our framework for KDD based on Evidence Theory. EDM consists of representation methods for data and knowledge and operators on the data and knowledge that together form the discovery process. Of the different types of operators within EDM, in this paper we limit our discussion to combination operators. We introduce a combination operator called the Proportional Belief Transfer operator and discuss its properties. In particular, we show how it differs from the Dempster-Shafer Orthogonal Sum.
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