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《Computer Simulation》 2018-01
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Mobile Internet Environment Abnormal High Dimensional Data Mining Simulation Automatically

YANG Jing-min;ZHANG Wen-jie;School of Computer Science,Minnan Normal University;Key Laboratory of Data Science and Intelligence Application,Fujian Province University;  
In order to improve the quality of network service,we need to research on automatic mining method of mobile high-dimensional abnormal data in Internet of things environment.Consequently,a method of automatic mining method of mobile high-dimensional abnormal data based on improved principal component analysis is proposed.Firstly,the principal component analysis method is used to input data sample and is standardized.Secondly,the covariance matrix of standard data matrix is calculated and the eigenvector of matrix is extracted.Thirdly,the coordinate transformation is used for each sample space of data feature,and different types of projection data are given.Moreover,the cumulative variance contribution rate of principal component of data is calculated,and fuzzy clustering center of multilayer space focused by abnormal data is obtained.By combining with training set of abnormal data class association,the information gain is got.After the attribute set of classification of abnormal data,the automatic mining of mobile high-dimensional abnormal data in Internet of things environment is realized.Simulation results show that the proposed method has high data mining accuracy.It effectively improves the service quality of Internet of things.
【Fund】: 国家自然科学基金(61701213);; 教育部产学合作协同育人项目(201602012019 201602021032);; 闽南师范大学科学研究资助项目(MX1602)
【CateGory Index】: TN929.5;TP311.13;TP391.44
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