Local outlier detection method based on density clustering

A technology of outlier detection and density clustering, which is applied in the field of outlier detection, can solve the problems that the selection of parameter values ​​is very sensitive, local outliers cannot be effectively detected, and it is difficult to determine

Inactive Publication Date: 2015-03-25
INFORMATION & TELECOMM COMPANY SICHUAN ELECTRIC POWER +1
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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 o

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  • Local outlier detection method based on density clustering
  • Local outlier detection method based on density clustering
  • Local outlier detection method based on density clustering

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Embodiment

[0059] Such as figure 1 , figure 2 As shown, a local outlier detection method based on density clustering described in this embodiment includes the following steps:

[0060] (a) Obtain the number of data families and cluster centers of the detected data set;

[0061] (b) by calculating the mean and standard deviation of the descriptive features of each data object in different data clusters;

[0062] (c) Use the 3sigma criterion to detect the outliers of each data cluster.

[0063] Step (a) is refined as follows:

[0064] (a1) Data set preprocessing: including data cleaning and data normalization. Data cleaning can manually delete noise data and data with missing values. To measure the influence of difference on data clustering and outlier detection process, it is necessary to normalize the data. Data normalization methods include maximum and minimum normalization, z-score normalization, and decimal scaling normalization.

[0065] (a2) Calculating the dissimilarity betwe...

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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...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 王电钢黄林黄昆常健陈龙潘可佳
Owner INFORMATION & TELECOMM COMPANY SICHUAN ELECTRIC POWER
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