Conditional evidence theory and its application in knowledge discovery

In this paper, we develop the conditional evidence theory and apply it to knowledge discovery in database. In this theory, we assume that a priori knowledge about generic situation and evidence about situation at hand can be modelled by two independent random sets. Dempster's rule of combination is a popular method used in evidence theory, we think that this rule can be applied to knowledge revision, but isn't appropriate for knowledge updating. Based on random set theory, we develop a new bayesian updating rule in evidence theory. More importantly, we show that bayesian updating rule can be performed incrementally by using Mobius transforms.