Fuzzy C-means clustering method of minimum variance optimization initial cluster center

An initial clustering center and initial clustering technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem that the result cannot get the optimal solution

Inactive Publication Date: 2017-11-07
CHANGZHOU COLLEGE OF INFORMATION TECH
View PDF0 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to provide a fuzzy C-means clustering method that optimizes the initial clustering center with th

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
  • Fuzzy C-means clustering method of minimum variance optimization initial cluster center
  • Fuzzy C-means clustering method of minimum variance optimization initial cluster center
  • Fuzzy C-means clustering method of minimum variance optimization initial cluster center

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0056] In order to make the technical solutions of the present invention clearer and clearer to those skilled in the art, the present invention will be further described in detail below in conjunction with the examples and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0057] Such as figure 1 As shown, the fuzzy C-means clustering method of a minimum variance optimization initial cluster center provided in this embodiment includes the following steps:

[0058] Step S1: Clustering the distance relationship between the input data set and the sample points;

[0059] Step S2: using the cluster analysis method to perform cluster analysis on the target data set to obtain cluster labels;

[0060] Step S3: Perform performance evaluation on the cluster labels obtained after the cluster analysis and the original labels according to the evaluation indicators.

[0061]Further, in this embodiment, in the step S1, the input data set is input...

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 fuzzy C-means clustering method of a minimum variance optimization initial cluster center, and belongs to the technical field of data mining and pattern recognition. The method comprises the steps of clustering a distance relation between an input data set and sample points; using a clustering analysis method for clustering analysis of a target data set to obtain a clustering label; and evaluating the performance of the clustering label obtained after the clustering analysis and an original label according to an evaluation index. The invention aims to solve the problem that a clustering effect of the fuzzy C-means is greatly affected by an initialized clustering center thereof and an optimal solution cannot be guaranteed. Based on the FCM algorithm, the selection of the initial clustering centers is first performed by selecting K sample points with the least variance on different regions as the initial cluster centers by taking the sample variance as heuristic information and by the sample field radius, and the algorithm does not need the setting of any parameters.

Description

technical field [0001] The invention relates to a clustering analysis method of a data set, in particular to a fuzzy C-means clustering method for optimizing an initial clustering center with minimum variance, and belongs to the technical field of data mining and pattern recognition. Background technique [0002] The traditional FCM algorithm selects the cluster centers randomly, which easily leads to unstable clustering results, and may even make the cluster centers converge to local extreme values. To solve the above problems, according to the compactness information of the sample distribution , the initial clustering center can be optimized according to the minimum variance. The initialization algorithm calculates the variance of the sample according to the spatial distribution information of the sample to obtain the closeness information of the sample, and selects the sample point with the minimum variance and the sample point within a certain range as the initial cluster...

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): G06K9/62
CPCG06F18/23213
Inventor 李学刚狄岚李斌李通明
Owner CHANGZHOU COLLEGE OF INFORMATION TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap