云计算环境下用户兴趣数据准确检测仿真

Accurate Detection of User Interest Data in Cloud Computing Environment

云计算环境下用户兴趣数据的准确检测,能够更好的提升数据管理质量。对用户数据的准确检测,需要对空间数据聚类过程进行自适应训练,获取数据聚类目标函数,完成对某一特征数据的准确检测。传统方法先给出数据检测空间的最优置信度,表述空间数据的采样时间长度,但忽略了获取数据聚类目标函数,导致检测精度偏低。提出基于空间自相关性的云计算环境下用户兴趣数据检测方法。上述方法先利用邻域对象的空间自相关性理论,获取离群空间数据和其邻域空间数据的距离,对各个数据进行聚类,获取数据均值参考点,并对生成的数据均值参考点进行拟合,提取数据高阶累积量特征,依据差分进化理论对空间数据聚类过程进行自适应训练,获取数据聚类目标函数,并完成对用户兴趣数据准确检测。实验结果表明,所提方法检测精度高,极大的提升了云计算环境下的数据管理质量。

This paper propose a detection method of user interest data under cloud computing environment based on spatial autocorrelation. Firstly, spatial autocorrelation theory of neighborhood object was used to acquire distance between outlier spatial data and its neighborhood spatial data. Then, each datum was clustered to acquire mean refer- ence point of data, and fitting was carried out for the generated mean reference point. Moreover, feature of high - or- der cumulate of data was extracted and adaption training was carried out for process of spatial data cluster according to differential evolution to acquire objective function of data cluster. Following conclusion can be drawn from experimen- tal results that the method has high detection precision, and it can improve quality of data management under cloud computing environment apparently.