DBSCAN abnormal data identification and detection method based on core point reservation

Abstract

The invention discloses a DBSCAN abnormal data identification and detection method based on core point reservation. The method comprises the following steps of S1, randomly dividing a data set into atraining set and a test set; s2, constructing a neighbor matrix Cre-M and an inverse neighbor matrix Cre-MR of the training set, and taking the inverse neighbor matrix Cre-MR as a basis for judging acore point. The invention relates to the technical field of anomaly identification and detection methods. According to the DBSCAN abnormal data identification and detection method based on core pointreservation, the density is redefined, the training set is clustered and labeled by using the density, and thus, data in a remaining test set is classified by using the label; a training set and a test set are divided through random sampling, so that the expansibility of the detection method is improved; meanwhile, only core points are adopted to establish a model, so that the influence of noise points, especially edge points, on a classification result is effectively reduced; through density definition, the weight of the sample points in the data set category can be well represented, and a better classification effect is achieved.

Description

technical field [0001] The invention relates to the technical field of abnormal identification and detection methods, in particular to an abnormal data identification and detection method based on DBSCAN with core point retention. Background technique [0002] Anomaly identification is a detection method for outlier sample points in a data set. Anomalies have rich connotations and may be noise, errors, or rare values. In the field of data mining, its generally accepted definition is a point that is produced by other mechanisms and deviates from the majority of observations. In this paper, the point opposite to the "outlier point" is called "normal point". [0003] As an important research direction, anomaly recognition has been widely used in real-world applications such as credit card fraud recognition, disease diagnosis and prevention, network intrusion, measurement error, and abnormal electricity consumption behavior. [0004] Statistical-Based Anomaly Identification M...

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Problems solved by technology

[0008] Aiming at the deficiencies of the prior art, the present invention provides an abnormal data identification and detection method based on DBSCAN with core point retention, which solves the problem that the identification method of abnormal data has almost no multivariate inconsistency check

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