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, we 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 Density based clustering approach and probabilistic queries and Decision rule based on rough set theory. The algorithm can select the decision rules on the basis of meeting the support and confidence, which can improve the accuracy and reasonableness of the decision rules mining.

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Uncertain Data Iterative Algorithm Rule Based Classification Associative Classification Density Based Clustering Probabilistic Queries And Rough Set Theory.