Gauss mixture model tree and incremental clustering method thereof

A Gaussian mixture model and clustering method technology, applied in database models, special data processing applications, instruments, etc., can solve problems such as unacceptable computational complexity and time complexity

Inactive Publication Date: 2014-05-28
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF3 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that traditional clustering methods become unacceptable in terms of computational complexity and time complexity with the increase of data volume and data growth rate in the era of big data, and propose a Gaussian mixture model tree and its incremental clustering 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
  • Gauss mixture model tree and incremental clustering method thereof
  • Gauss mixture model tree and incremental clustering method thereof
  • Gauss mixture model tree and incremental clustering method thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0062] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0063] The incremental clustering method adopted in this embodiment includes data insertion, cluster tree update, data deletion and clustering result determination. The relationship between these four technical links is: for each new data, it needs to be inserted into the existing Gaussian mixture model tree, and then the clustering tree is updated according to the inserted result; with the insertion of new data, check that it has been inserted into the cluster Whether the data of the tree needs to be deleted, and if it needs to be deleted, delete the data; when all the data is read, the clustering result is determined. Process such as figure 1 shown.

[0064] The structure of data clustering is as figure 2 Shown, G 1 to G 5 are five leaf nodes, corresponding to a single Gaussian component, corresponding to the densest data distribution; GMM 1...

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 relates to a Gauss mixture model tree and an incremental clustering method thereof, and belongs to the field of clustering technologies in computer application technologies. The method includes 1, providing a novel clustering structure, namely a Gauss mixture model tree (clustering tree); 2, on the basis of 1, providing an incremental clustering method based on the Gauss mixture model tree. The incremental clustering method includes four steps of inserting data, updating the clustering tree, deleting the data and determining clustering results. The relation of the four steps includes inserting each new data to an existing Gauss mixture model tree as required, and updating the cluster tree according to inserting results; by means of inserting the new data, detecting whether the data inserted into the clustering tree needs to be deleted or not; if so, deleting the data; after all data is read, determining the clustering results. The method has good effects in accuracy, executing efficiency and stability of clustering.

Description

technical field [0001] The invention relates to an incremental clustering structure—a Gaussian mixture model tree, and an incremental clustering method thereof, which belong to the technical field of clustering in computer application technology. Background technique [0002] With the advent of the era of big data, data plays an increasingly important role in people's life and work. At present, massive amounts of data already exist on the Internet, and the amount is still growing at a high speed. For example, according to Alexa, the most famous online photo sharing site www.flickr.com , ranked 23rd in the world in terms of visits, with an average monthly visits of 60 million, and a total of more than 5 billion photos uploaded. In order to automatically classify data better, clustering technology has received more and more attention. [0003] At present, most of the existing clustering methods are static clustering methods, that is, the entire data set needs to be scanned ...

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
CPCG06F16/285
Inventor 刘峡壁伍艺万玉钗
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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