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

Adaptive PSO-PFCM clustering method for big data mining and clustering

A clustering method and big data technology, applied in the field of big data processing, can solve the problems of inconvenient fuzzy clustering of different data sets, artificially setting the number of clusters, etc., and achieve the effect of reducing manual work and achieving excellent performance.

Inactive Publication Date: 2019-07-02
UNIV OF ELECTRONIC SCI & TECH OF CHINA
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Aiming at the above-mentioned deficiencies in the prior art, the adaptive PSO-PFCM clustering method for big data mining clustering provided by the present invention solves the traditional fuzzy clustering algorithm that needs to artificially set the number of clusters, and the fuzziness of different data sets. Clustering is very inconvenient problem

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
  • Adaptive PSO-PFCM clustering method for big data mining and clustering
  • Adaptive PSO-PFCM clustering method for big data mining and clustering
  • Adaptive PSO-PFCM clustering method for big data mining and clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0079] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0080] The present invention mainly uses two clustering ideas:

[0081] 1. Fuzzy clustering: Fuzzy clustering analysis is a mathematical method to classify things according to certain requirements when it involves fuzzy boundaries between things. The boundaries between things, some are exact, others are fuzzy. The boundary between the degree of facial resemblance in the sample population is blurred, and the boundary betwee...

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 an adaptive PSO-PFCM clustering method for big data mining and clustering. Module density calculation and the self-adaptive clustering number of the module density calculationare applied to the big data clustering process, so that the best clustering number can be automatically determined by the big data clustering method which originally needs to manually set clustering data, and the PSO-PFCM algorithm is allowed to be suitable for clustering different original data sets while retaining the excellent performance of the device in the big data processing process, so that a large amount of manual work is reduced.

Description

technical field [0001] The invention belongs to the technical field of big data processing, and in particular relates to an adaptive PSO-PFCM clustering method used in the process of big data mining. Background technique [0002] Clustering Analysis (Clustering Analysis, CA) is an unsupervised machine learning method that studies objects based on mathematical methods and divides given objects. CA is to divide the target object into the corresponding multiple clusters with a certain metric, in order to make the clusters have better similarity, but there are obvious dissimilarities among the clusters. In addition, it can obtain additional useful information from the data without domain knowledge, and can intuitively and quickly reflect the characteristics of the data. However, the traditional fuzzy clustering algorithm needs to set the number of clusters artificially, and different data sets cannot adaptively judge the number of categories, which makes fuzzy clustering of dif...

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): G06F16/2458G06F17/50G06K9/62
CPCG06F30/20G06F18/23
Inventor 曹建蜀王晟
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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