Anomaly detection based on the multiphase clustering and naive bayes

Anomaly detection method was used for calibration data concentration significantly different from other data objects. In this paper, the multiphase clustering aimed at resolving the import of noise data and the lack of the attributive sample, and changing the traditional passive learning of bayes for active learning ways to structure the superior performance classifier. In the preprocessing stage, a clustering algorithm based on density is introduced to handle noise data. And the output of the densitybased clustering algorithm can be used as the input of Kmeans, which responsible for handling the training samples with absent values. At classification time, we introduce adaboost algorithm into naive bayes to generate a more effective classifier.

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