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
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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 an

Method used

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  • 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

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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

...

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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

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Application Information

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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
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