Method and system for stable and efficient self-adaptive clustering

A clustering method and self-adaptive technology, which can be used in special data processing applications, instruments, electrical digital data processing, etc., and can solve problems such as high computational overhead.

Active Publication Date: 2013-07-17
WUXI TSINGHUA NAT LAB FOR INFORMATIONSCI & TECH INTERNET OF THINGS TECH CENT
View PDF4 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to propose an adaptive, stable and efficient clustering method and system to solve the problem of large computational overhead

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
  • Method and system for stable and efficient self-adaptive clustering
  • Method and system for stable and efficient self-adaptive clustering
  • Method and system for stable and efficient self-adaptive clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings and through specific implementation methods.

[0029] The process of the first embodiment of an adaptive, stable and efficient clustering method of the present invention is as follows figure 1 Shown:

[0030] Step 101, obtain the set of input data as p={p 1 ,...p n}, the collection includes n input data, and obtains the threshold θ of the clustering radius;

[0031] Step 102, put p i and the input data p in the set i The input data whose distance is less than the threshold θ are added to the input data p i The corresponding candidate cluster C pi , , input data p i Indicates the i-th input data in the set;

[0032] Step 103, let the candidate cluster C pi The input data in is m, and the function d(p i ,p j ) for two input data p i ,p j The distance between, calculate the input data p i The probability of being the cluster center is ...

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 method and a system for stable and efficient self-adaptive clustering. The method comprises the following steps of: a, obtaining a set p of input data from p1 to pn, in which n input data is included in the set, and obtaining a threshold value theta of cluster radius; b, adding pi and input data in the set with a distance from the input data pi of smaller than the threshold value theta in a candidate cluster Cpi corresponding to the input data pi, in which the input data pi represents the i-th input data in the set; and c, defining m input data in the candidate cluster Cpi, using a function d (pi, pij) as a distance between the two input data pi, pj, and calculating the input data pi as the probability of a cluster center. The method and the system are applied for establishing a stable and efficient self-adaptive cluster system; the amount of final clusters is not required to be preset, so that the system has calculation efficiency and can realize calculation complexity of o(m2), and the method and the system can be suitable for various mobile intelligent terminals at present.

Description

technical field [0001] The invention relates to the technical field of computer information processing, in particular to an adaptive, stable and efficient clustering method and system. Background technique [0002] With the rapid growth of computer information, people's demand for processing various computer information is becoming stronger and stronger. As a very important class of algorithms in information processing, clustering algorithms provide basic clustering functions for various data management, artificial intelligence, and machine learning, and play an important role in various information processing. [0003] Today, with the widespread application of smart mobile terminals, various information services based on smart mobile devices have emerged. They need to provide efficient and stable services to various smart terminals, and a large number of services need to use clustering algorithms, such as mobile Clustering of social friends in social networks, clustering o...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 张兰刘云浩
Owner WUXI TSINGHUA NAT LAB FOR INFORMATIONSCI & TECH INTERNET OF THINGS TECH CENT
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