Rough set anlaysis for uncertain data classification

Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, out-dated sources and sampling errors. As a result there is a need for tools and techniques for mining and managing uncertain data. These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper proposes a Rough Set method for handling data uncertainty. Rough set is a mathematical theory for dealing with uncertainty. A pair of crisp sets, called the lower and upper approximations of the set, represents a rough set. In this extension if the data point is in the lower approximation, we are sure that it is in the set. If it is not in the upper approximation, we are sure that it is not in the set. Uncertainty implies inconsistencies, which are taken into account, so that the produced are categorized into certain and possible with the help of rough set theory Experimental results show that proposed model exhibits reasonable accuracy performance in classification on uncertain data.