International Journal of Computer & Organization Trends(IJCOT)
Traditional data mining clustering work with data whose values are known and precise. The authors expand such classification algorithms to facilitate data with uncertain data. Value uncertainty appears in many applications in the course of the data selection process. For one example resources of uncertainty involve quantization/measurement flaws, data repudiate, various repeated estimations. Through the use of uncertainty, the value of causing data item will often be symbolized not by virtually any value, but by several values inducing a probability distribution.