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Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, 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. The authors propose that when data mining is performed on uncertain data, data uncertainty has to be considered in order to obtain high quality data mining results. They present a Probabilistic Neural Network model which is suitable for classification problems. This model constitutes an adaptation of the classical RBF network where the outputs represent the class conditional distributions.
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