Methods for Handling Uncertainty in Decision Support System
Data uncertainty is common in real world applications due to various causes, including imprecise measurements, network latency, out dated sources and sampling errors. These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, the authors are describing the various ways for managing, mining and handling uncertainty. Uncertain data are inherent in many applications. Recently, considerable research efforts have been put into the field of managing uncertain data. There are many algorithms to handle the uncertainty. Some of them are iterative algorithm, Rule based classification approach, Associative classification model and probabilistic queries and Decision rule based on rough set theory.