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

Laplacian centrality-based peak clustering method

A clustering method and peak technology, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of low accuracy and setting parameters in advance, so as to achieve high algorithm accuracy and fast speed without parameters The effect of clustering

Inactive Publication Date: 2017-09-22
ZHEJIANG UNIV OF TECH
View PDF0 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of existing clustering methods such as low accuracy and the need to set parameters in advance, the present invention proposes a peak clustering based on Laplacian centrality with high clustering accuracy and no need to set parameters in advance method

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
  • Laplacian centrality-based peak clustering method
  • Laplacian centrality-based peak clustering method
  • Laplacian centrality-based peak clustering method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0027] The present invention will be further described below in conjunction with the accompanying drawings.

[0028] refer to figure 1 , a peak clustering method based on Laplacian centrality, including the following steps:

[0029] Step 1: Establish a data set model D={v i}, i=1...n, where v i is any data point in data set D, data point v i and v j The distance between is d ij ;

[0030] Step 2: Transform the data set D to be classified into a weighted complete graph model G. A node in G represents a data point in the data set, and the weight of the edge between any two nodes is the weight between the corresponding two data points. to obtain the weight matrix of the weighted complete graph G:

[0031]

[0032] where w i,j for node v i with v j The edge weight between;

[0033] Step 3: According to the weight matrix W(G), calculate the diagonal matrix that represents the sum of the weights from each node to all other nodes:

[0034]

[0035] in, is node v ...

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 Laplacian centrality-based peak clustering method. The method comprises the steps of firstly converting a to-be-classified data set into a weighted complete graph, taking each data point as a node, and taking a distance between two data points as a weight value of an edge between the corresponding two nodes; calculating and evaluating local importance of each node of a network by using Laplacian centrality; obtaining cluster centers, wherein the cluster centers are nodes with local density centers with higher Laplacian centrality in comparison with neighbor nodes around, and relatively long distances from nodes with higher Laplacian centrality; and applying class cluster tags to all the class cluster centers, attributing a node free of the class cluster tag to a class cluster which a node with a shortest distance and the class cluster tag belongs to, and obtaining the tag of the class cluster. According to the method, an algorithm for evaluating the importance of the nodes of the network is introduced in the aspect of evaluating the importance of the to-be-classified data points; the algorithm accuracy is high; and parameter-free quick clustering can be realized.

Description

technical field [0001] The present invention relates to the field of machine learning, in particular to a peak clustering method based on Laplacian centrality. Background technique [0002] Machine learning is an important branch that is very active in the field of artificial intelligence. Its research goal is mainly to allow computers to simulate or realize human learning behaviors to acquire new knowledge or skills. The clustering method belongs to the unsupervised learning algorithm in machine learning, which can extract the inherent hidden structure of the data, gather the data points with similarity attributes into a cluster, and the data points inside the cluster have greater similarity, while The similarity of data points of different clusters is relatively low. Clustering methods have a wide range of applications in many fields of computer science and other disciplines. [0003] Researchers have done a lot of useful work on clustering algorithms, such as partition-...

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
CPCG06F18/2321
Inventor 杨旭华陈果朱钦鹏
Owner ZHEJIANG UNIV OF TECH
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