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Deviation feature-based outlier mining method

A technology of outlier points and outlier factors, applied in the field of outlier point mining based on deviation characteristics, can solve the problem of high time complexity of LOF detection algorithm, achieve the effect of reducing time complexity, reducing calculation time, and improving detection efficiency

Active Publication Date: 2018-01-09
HARBIN ENG UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the time complexity of the LOF detection algorithm is high, and from the perspective of the deviation feature, a kind of outlier mining method based on the deviation feature of an improved fast LOF detection algorithm is proposed

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  • Deviation feature-based outlier mining method

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Embodiment Construction

[0031] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to illustrations.

[0032] At present, both at home and abroad, active research is being carried out on outlier mining methods. Scholars have proposed a variety of model methods and corresponding algorithms. They have their own characteristics for different types of outliers and specific practical problems. . On the basis of previous studies, the present invention aims at the problem of high time complexity of the LOF outlier detection algorithm, and proposes an improved fast LOF detection algorithm.

[0033] combine figure 1 It is a diagram of the mining process of the present invention; the mining process is from obtaining the definition of outliers to data preprocessing and then implementing the outlier detection method, obtaining evaluation and expl...

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Abstract

The invention discloses a deviation feature-based outlier mining method. The method comprises The following steps of: (1) dividing each dimensionality of a data set into h uniformly-spaced intervals so as to divide the whole data set into hd grids; (2) associating each data point with the grids, and if one grid does not comprise a data point, not considering the grid; (3) for each grid in a spaceformed via the division, solving a centroid of the grid and calculating a local outlier factor of the centroid; and (4) calculating a local outlier factor of each data object, wherein the local outlier factors of the objects in the data set are equal to the outlier factors of the centroids of the grids. According to the method, when outliers in the data set are detected, the data space is dividedinto the grids by adoption of an F-LOF detection algorithm, and the local outlier factors of the data points are calculated on the basis of the centroids of the grids, so that the calculation time isshortened, the detection efficiency is improved and the superiority is embodied.

Description

technical field [0001] The invention relates to the field of outlier point mining, in particular to an outlier point mining method based on deviation characteristics. Background technique [0002] Outlier mining is an important research branch of data mining, which is effectively applied in log analysis, intrusion detection, quality control and other real-life fields. Outlier mining includes outlier detection and outlier analysis. Outlier detection is to use appropriate algorithms to detect abnormal behaviors that are different from most behaviors, and to obtain some real but rare outlier information; outlier analysis is to conduct in-depth analysis of the detected information to obtain knowledge or pattern; its 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 deep...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 王红滨冯梦园何鸣王念滨尹新亮顾正浩苏畅童鹏鹏曾庆宇张海彬
Owner HARBIN ENG UNIV
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