Local outlier detection method based on density clustering

Abstract

The invention discloses a local outlier detection method based on density clustering. The method includes the steps: (a) acquiring data cluster number and clustering center of a to-be-detected dataset; (b) calculating the mean and standard deviation of description features of each data object in different data clusters; (c) detecting according to 3sigma criterion to obtain outliers of each data cluster. By adoption of the local outlier detection method based on density clustering, easiness in setting of parameters is realized, the method is applicable to datasets with different density regions and arbitrary shapes and can be used for detection of local outliers, accuracy of outlier detection results is high, algorithms are insensitive to selection of parameter values, and excellent robustness is achieved.

Description

technical field [0001] The invention relates to the field of outlier point detection, in particular to a local outlier point detection method based on density clustering. Background technique [0002] Outlier detection is a branch of data mining whose task is to identify observations whose data characteristics are significantly different from other data objects. Outlier detection is very important in data mining, because if the anomalies are caused by the variation of inherent data, analyzing them can reveal deeper, potential and valuable information hidden in it. Therefore, outlier detection is a very meaningful research direction. [0003] Data mining expert Hawkins defines outliers as: "Outliers are data objects that are distinctive in the data set, and their performance is so different from other data objects that people suspect that these data objects are not random deviations, but caused by produced by a completely different mechanism". This definition reveals the n...

AI Technical Summary

Problems solved by technology

There are also many algorithms that are originally designed to perform cluster analysis, but further extend the clustering results to the judgment of outliers.

At present, most of the outlier detection methods based on clustering technology are often not optimal, and many of the detected outliers are global outliers, which cannot effectively detect local outliers.

In addition, the parameter settings of many clustering algorithms usually rely on experience, are difficult to determine, and are very sensitive to the selection of parameter values.

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