Abnormal data detection method for multidimensional datasets based on spectral clustering

Abstract

Aiming at the problem that the detection of abnormal data in multidimensional data sets is not processed by dimensionality reduction, which leads to low detection accuracy, high false positive rate and long detection time, a method for detecting abnormal data in multidimensional data sets based on spectral clustering is proposed. Firstly, the data in the multidimensional data set is clustered by Laplacian matrix to preliminarily classify the data; secondly, the local linear embedding (LLE) algorithm is used to reduce the dimensionality of the classified data, and the high-dimensional data set is expressed by feature vectors to remove redundant information in the multidimensional data set; finally, the processed multidimensional data set is input into the support vector machine model, and the abnormal data is detected according to the calculation of regression estimation value. Experimental results show that the proposed algorithm has higher accuracy, lower false positive rate and shorter detection time for detecting abnormal data in multidimensional data sets.