Big data space-oriented data local density clustering method

A data local and density clustering technology, which is applied to computer parts, instruments, character and pattern recognition, etc., can solve problems such as poor clustering effect, difficult data processing, unreasonable algorithm structure, etc., to achieve effective mining and The effect of the analysis

CN111652305APending Publication Date: 2020-09-11上海勃池信息技术有限公司
0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2020-09-11

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The invention discloses a big data space-oriented data local density clustering method. The method comprises the following steps of presetting a set density parameter and a distance adjustment parameter; calculating a local density value of each data point; finding a maximum local density point in the data set in the calculation process; calculating a dynamic neighborhood radius to obtain a firstsub-cluster with direct density; similarly, obtaining each density data cluster of the original big data set; and according to the dynamic neighborhood radius and the adaptive density reachable distance of each attraction point, carrying out data set division. According to the method, the defects in the prior art are overcome, and effective clustering of clusters with different sizes, different forms and different densities is realized, so that help is provided for subsequent effective mining and analysis of big data.
Need to check novelty before this filing date? Find Prior Art

Description

technical field

[0001] The invention relates to the technical field of computer big data processing, in particular to a data local density clustering method for big data space. Background technique

[0002] Big data objects have the complexity of the data space distribution state, such as the distribution patterns of data objects of different sizes, shapes and densities in the data space. Use efficient attribute weighting and density clustering algorithms to calculate the distribution density of data objects in the data space, determine the density attraction point, that is, the extreme point, and the density of the data object to the density attraction point, so as to realize clusters of different sizes, shapes and densities Effective clustering, and then realize the effective mining and analysis of a large amount of data. All in all, the massiveness of data is an important feature of big data. How to realize the effective clustering analysis of big data spatial data objec...

Examples

Embodiment Construction

[0027] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention.

[0028] Such as Figure 1-2 As shown, a data local density clustering method for large data space, including the following steps:

[0029] Step S1: preset the density parameter σ and the distance adjustment parameter coefR;

[0030] Step S2: According to the following formula, the P of each data point in the data set i (i=1,2,...) local density values ​​are calculated;

[0031]

[0032] In the formula, the Gaussian function of the right formula represents each data point pair x i point; σ is the density parameter, which determines the change gradient of the density function.

[0033] Step S3: compare the density value of each point, search for the maximum local density point, and use it as the densit...