Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Clustering boundary detection method, system and equipment based on centroid migration and medium

A boundary detection and centroid technology, which is applied in complex mathematical operations, instruments, character and pattern recognition, etc., can solve problems such as complex calculations, and achieve the effects of solving complex calculations, improving accuracy, and improving detection efficiency

Pending Publication Date: 2022-02-08
中国航天科工集团第二研究院
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the calculation of the existing clustering boundary detection algorithm is relatively complicated. Therefore, developing a clustering boundary detection method with a simple calculation method is a necessary means to improve the efficiency of clustering boundary detection, thereby helping to improve the accuracy of the clustering algorithm.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Clustering boundary detection method, system and equipment based on centroid migration and medium
  • Clustering boundary detection method, system and equipment based on centroid migration and medium
  • Clustering boundary detection method, system and equipment based on centroid migration and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0042] This embodiment is used to provide a cluster boundary detection method based on centroid offset, such as figure 1 As shown, the boundary detection method includes:

[0043] S1: Calculate the centroid offset index corresponding to each data point in the data set to be clustered;

[0044] Specifically, S1 may include:

[0045] 1) For the i-th data point in the data set to be clustered, calculate the Euclidean distance between the i-th data point and the j-th data point in the data set to be clustered, and obtain the Euclidean distance set corresponding to the i-th data point ; i=1, 2, ..., N; j = 1, 2, ..., N; N is the number of data points in the data set to be clustered;

[0046] The data set X to be clustered includes N data points, and each data point corresponds to a feature vector, and the feature dimension of the feature vector is n. Then the data set X to be clustered can be recorded as: X={x 1 , x 2 ,...,x N}∈R n×N , x i is the feature vector of the i-th ...

Embodiment 2

[0063] This embodiment is used to provide a cluster boundary detection system based on centroid offset, such as image 3 As shown, the boundary detection system includes:

[0064] The centroid offset calculation module M1 is used to calculate the centroid offset index corresponding to each data point in the data set to be clustered;

[0065] The boundary point detection module M2 is configured to compare the centroid shift index with a preset threshold, and determine the boundary points in the data set to be clustered according to the comparison result.

[0066] This embodiment is used to provide a cluster boundary detection device based on centroid offset, including:

[0067] processor; and

[0068] a memory in which computer readable program instructions are stored,

[0069] Wherein, the boundary detection method described in Embodiment 1 is executed when the computer-readable program instructions are executed by the processor.

Embodiment 4

[0071] A computer-readable storage medium, on which a computer program is stored, is characterized in that, when the program is executed by a processor, the steps of the boundary detection method described in Embodiment 1 are implemented.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a clustering boundary detection method, system and equipment based on centroid migration and a medium, relates to the technical field of clustering algorithm optimization, and aims to solve the problem that an existing boundary detection method is complex in calculation. The boundary detection method comprises the steps of firstly calculating a centroid offset index corresponding to each data point in a to-be-clustered data set, then comparing the centroid offset index with a preset threshold value, and determining a clustering boundary point according to a comparison result, so that the clustering boundary point can be rapidly determined, the calculation is simple and convenient, the problem of complex calculation of the existing clustering boundary point is solved, the detection efficiency of the clustering boundary is significantly improved, and the precision of the clustering algorithm is improved. The clustering boundary detection method, system and equipment based on centroid migration, and the medium provided by the invention are used for optimizing the clustering algorithm and improving the clustering precision of the clustering algorithm.

Description

technical field [0001] The present invention relates to the technical field of clustering algorithm optimization, in particular to a centroid offset-based clustering boundary detection method, system, device, and medium. Background technique [0002] Clustering refers to the aggregation of data points without labels in the feature space into several sets according to their own characteristics. The data points belonging to the same set are similar, and the data points belonging to different sets are very different. In the era of big data, clustering algorithm is an effective means of data analysis and information mining, and has been successfully used in fields such as face recognition and web recommendation systems. [0003] On the premise that the number of clusters is known k, the existing clustering algorithms generally find k points as the centers of each cluster, and then assign cluster labels to the unmarked points according to the distance from the unmarked points to ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/62G06F17/16
CPCG06F17/16G06F18/23
Inventor 曲徽马喆孙科武魏琦肖柯
Owner 中国航天科工集团第二研究院
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com