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

Association rule optimization algorithm of subjective interest measures on mass data sets

A mass data and optimization algorithm technology, applied in the direction of electrical digital data processing, special data processing applications, calculations, etc., can solve problems such as difficulty in judging optimization effects, limited analysis and clutter, etc.

Active Publication Date: 2014-05-21
UNIV OF ELECTRONIC SCI & TECH OF CHINA
View PDF2 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

On the other hand, the degree of interest generally involves only one type of interest degree, and the degree of refinement of analysis is limited; the calculation model of interest degree is single and messy, and the optimization effect is difficult to judge

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
  • Association rule optimization algorithm of subjective interest measures on mass data sets
  • Association rule optimization algorithm of subjective interest measures on mass data sets
  • Association rule optimization algorithm of subjective interest measures on mass data sets

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0067] In order to make the present invention more obvious and understandable, the present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments:

[0068] The steps of subjective interest degree optimization algorithm:

[0069] 1 get data

[0070] Example data description: GA represents the grades of professional courses, and there are 7 professional courses GA1~GA7 in total; GB represents the grades of basic courses, and there are 7 basic courses GB1~GB7 in total. The grades of each course are represented by 1, 2, and 3, with 1 being the worst, 2 being average, and 3 being excellent. The following 12 rules are mined using the association rule algorithm. The characteristics of these rules are that the antecedents of the rules are all GA, and the postconditions of the rules are all GB.

[0071] serial number

rule

serial number

rule

R1

GA1-3→GB2-3

R7

GA4-1→GB7-2

...

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 provides an association rule optimization algorithm of subjective interest measures on mass data sets. According to the algorithm, composite templates are used for simultaneous optimized analysis and is divided into an overall impression knowledge template (GI) and a relative precise knowledge template (RPC); due to the classification, the user implication expression range is extended, optimization for association rules from different focuses is facilitated; besides, template limiting and containing functions are turned to be reflected on different interest measures which are refined into four types including consistency, consequent unpredictable degree, antecedent unpredictable degree and unpredictable degree, so that the optimization is quite clear; and interest measure calculation models of the composite templates are optimized and combined, and calculation of the interest measures can adapt to a composite analysis environment reasonably.

Description

technical field [0001] The present invention is an association rule optimization algorithm related to subjective interest in massive data sets. The method can find interesting associations or related links between item sets in a large amount of data, and can help many business decision-making, such as classification design, intersection Shopping and sale analysis, etc., belong to the field of association rule optimization algorithm in association rule mining. Background technique [0002] The large number of association rules derived from association mining of massive data brings difficulties to the judgment of analysts and decision-makers, and the traditional association rule mining algorithm based only on the support-confidence framework cannot point out the rules that users are really interested in , which brings inconvenience to the user's analysis of the exported rules, and rule optimization has become an effective means to improve the quality of rules and discover valu...

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): G06F19/00
Inventor 牛新征周冬梅侯孟书杨健
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