Anomaly detection algorithm based on relative density

A relative density, anomaly detection technology, applied in computing, other database retrieval, special data processing applications, etc., to achieve the effect of high accuracy, high accuracy and robustness

Inactive Publication Date: 2017-08-18
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

This method is a good anomaly detection method, but it has certain defects in sparse data sets

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  • Anomaly detection algorithm based on relative density
  • Anomaly detection algorithm based on relative density
  • Anomaly detection algorithm based on relative density

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Embodiment Construction

[0027] A relative density-based anomaly detection algorithm proposed by the present invention will be described in detail below in conjunction with the accompanying drawings.

[0028] Such as figure 1 As shown, the anomaly detection algorithm based on relative density of the present invention comprises the following steps:

[0029] Step 1) Determine the algorithm input variables, including the unlabeled sample set D to be detected, the number k of nearest neighbors, k is randomly selected from [50, 200], and the number n of abnormal points obtained according to prior knowledge;

[0030] Step 2) For a given sample point p in data D, according to the distance function dist(), which is Euclidean distance, obtain the k nearest neighbors N of point p k (p);

[0031] Step 3) Compute points p to N k The average distance dist of all points in (p) knn (p), then according to dist knn (p) can calculate the density density(p) of point p;

[0032] Step 4) For set N k Each data point...

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Abstract

The invention discloses an anomaly detection algorithm based on relative density and belongs to the fields of machine learning and data mining. According to the anomaly detection algorithm, a locally relative density method is adopted based on the nearest neighbor ideology, an abnormal point is judged according to the difference between density of data points and density of neighbors of the data points, and the larger the relative density difference between a given data point and a neighbor, the higher the abnormity of the data point. Compared with a traditional density-based mode, the relative density method has higher accuracy, the problem that a local anomaly cannot be detected through a distance-based method can be solved, and the defect that a density-based method is invalid to sparse data can be overcome. Points of different anomaly types can be detected for different data.

Description

technical field [0001] The invention relates to the technical fields of data mining and machine learning, in particular to an anomaly detection algorithm based on relative density. Background technique [0002] Anomaly detection is a very important task in the field of data mining and machine learning. Anomaly detection is mainly about mining rare patterns in large and complex datasets. In practical applications, the information contained in the abnormal pattern is often more important than the information contained in the normal pattern. For example, it is more useful to mine cancer symptoms (rare patterns) in medical diagnosis than normal information. Anomaly detection technology is widely used in various fields, such as network traffic intrusion, credit card detection, medical health detection, etc. [0003] Machine learning and data mining have defined many outlier detection methods, such as distribution-based [1], distance-based [2][3], density-based [4], cluster-base...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/90
Inventor 关东海陈凯袁伟伟
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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