Accurate detection of user interest data in cloud computing environment

The accurate detection of user interest data in cloud computing environment can improve the quality of data management. For the accurate detection of user data, we need the adaptive train for the spatial data clustering process obtaining the data clustering objective function, and then complete the accurate detection of a characteristic data. This paper proposed the method of user interest data detection in cloud computing environment based on spatial autocorrelation and differential evolution theory. The method used spatial autocorrelation theory of neighborhood object to obtain the distance between outlier spatial data and its neighborhood spatial data, clustering all data for obtaining the data mean reference point, fitting the generated data mean reference point. The higher-order cumulant feature of data row was extracted. We used the differential evolution theory for the adaptive training on the clustering process of spatial data, the data clustering objective function was obtained. On this basis, we complete the user interest data detection. Experimental results show that the proposed method can accurately detect the user interest data in data space, and the false alarm rate of the proposed method is well below the traditional method. In the case of the same amount of data, the running time of the proposed method is lower than the traditional method. The proposed method has high detection accuracy and greatly improves the quality of data management in the cloud computing environment.

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