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Mixing attribute data flow cluster method based on reinforcement cluster edge detection of grid

A data stream clustering and edge detection technology, applied in instruments, character and pattern recognition, computer parts, etc., can solve problems such as low clustering quality and poor ability to handle edge grids

Inactive Publication Date: 2015-12-23
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings of the existing hybrid attribute clustering methods, such as low clustering quality and poor ability to process edge grids, the present invention provides a grid-based enhancement with high clustering quality and strong ability to process edge networks Mixed attribute data stream clustering method for cluster edge detection

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  • Mixing attribute data flow cluster method based on reinforcement cluster edge detection of grid
  • Mixing attribute data flow cluster method based on reinforcement cluster edge detection of grid
  • Mixing attribute data flow cluster method based on reinforcement cluster edge detection of grid

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Embodiment Construction

[0067] The present invention will be further described below in conjunction with the accompanying drawings.

[0068] refer to Figure 1 ~ Figure 4 , a grid-based mixed attribute data stream clustering method for strengthening clustering edge detection, the clustering method includes the following steps:

[0069] 1) Grid preprocessing process: the evolutionary division of the d-dimensional data space where the data object is located. Since the mixed attribute includes numerical attributes and type attributes, the numerical data of each dimension is divided into P according to the size of the grid granularity, etc. In addition, divide the fractal type data of each dimension according to the number of possible values ​​in its domain. Divide the data space into several disjoint hypercubes, and each rectangular grid unit can be described as S 1,j1 ×S 2,j2 ×...×S d,jd . where attribute S i , ii Expressed in S iThe interval obtained on this dimension.

[0070] 2) Online grid ...

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Abstract

The invention discloses a mixing attribute data flow cluster method based on reinforcement cluster edge detection of a grid, comprising steps of 1) a grid pre-processing process, 2) an online grid maintenance process, and 3) an off-line cluster process. The grid pre-processing process includes steps of dividing a d dimension data space where the data object is positioned, dividing each dimension of numeric data into P equally-divided sections according to the size of the grid granularity due to the fact that the mixing attribute comprises a numeric value attribute and the classification attribute, dividing classification data of each dimension according to the possible number of the value in the domain and dividing a data space into a plurality of measure polytopes which are not mutually intersected, wherein each rectangle grid unit expression is S1,J1*S2,j2*...*Sd,jd, wherein the attributes Si,(i<d) is an attribute on the data space S, and the subscript ji expresses the section obtained on the dimension of the Si. The invention provides a mixing attribute data flow cluster method based on reinforcement cluster edge detection of a grid which is high in cluster quality and strong in processing rim network.

Description

technical field [0001] The present invention belongs to a clustering method involving mixed data streams. Aiming at the existing problems of mixed attribute data stream clustering, a grid-based mixed attribute data stream clustering method that strengthens cluster edge detection is proposed, which includes both Numerical attribute data and categorical attribute data realize a data stream clustering method for arbitrary shapes, including outliers and cluster numbers that are automatically determined. Background technique [0002] With the continuous development of communication technology and hardware equipment, data stream mining technology has great application prospects in real-time monitoring systems, meteorological satellite remote sensing, and network traffic monitoring. Traditional clustering algorithms cannot be applied to data flow objects, and data flow puts forward new requirements for clustering algorithms as follows [1] : 1. There is no need to assume the number...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2323
Inventor 陈晋音何辉豪陈军敢杨东勇
Owner ZHEJIANG UNIV OF TECH
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