Detection method of abnormal data in cube based on spectral clustering

Due to the lack of dimension reduction in the process of cube abnormal data detection，the detection accuracy of abnormal data in the cube is low，the error detection rate is high，and the detection time is long. Therefore，a cube abnormal data detection method based on spectral clustering is proposed. Cluster the data in the multidimensional data set through Laplace matrix，preliminarily classify the data，use LLE algorithm to reduce the dimension of the classified data，express the high-dimensional data set with eigenvectors，remove the redundant information in the multidimensional data set，input the processed multidimensional data set into the support vector machine model，and complete the detection of abnormal data according to the calculation of regression estimates. Experimental results show that the proposed algorithm has higher accuracy，lower false detection rate and shorter detection time. © 2023 Editorial Board of Jilin University. All rights reserved.