A method to generate BBA(basic belief assignment)based on cluster analysis is proposed to focus the problem that the mass function is hard to determine when the frame is unknown. The method tackles the situation whether the frame of discernment is known or not. A clustering analysis method is applied to extract cluster features and models of cluster features are constructed with the samples. Then the distances between different cluster feature models are calculated to represent differences between sample attributes and then the similarities of them are obtained. Finally
the values of similarities are normalized to get the BBA. The analysis results of classifying the Iris dataset and Wine dataset show that the proposed method is less dependent on the length of samples and the classification accuracy in Wine dataset is 100%. Monitoring information series by applying the method to a compressor unit system proves the effectiveness of the method
and the condition of monitoring information can be clearly recognized.
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references
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