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An Association Rule Optimization Algorithm for Subjective Interestingness in Massive Datasets

A technology of mass data and optimization algorithms, applied in the fields of electrical digital data processing, special data processing applications, calculations, etc., can solve problems such as difficulty in judging optimization effects, clutter, and limited analysis and refinement, and achieve the effect of enriching user meanings

Active Publication Date: 2017-04-19
UNIV OF ELECTRONICS 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 analysis is limited; the calculation model of interest degree is single and messy, and the optimization effect is difficult to judge

Method used

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  • An Association Rule Optimization Algorithm for Subjective Interestingness in Massive Datasets
  • An Association Rule Optimization Algorithm for Subjective Interestingness in Massive Datasets
  • An Association Rule Optimization Algorithm for Subjective Interestingness in Massive Datasets

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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] Numbering rule Numbering rule R1 GA1-3→GB2-3 R7 GA4-1→GB7-2 R2 GA4-3→GB4-3 R8 GA6...

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Abstract

An association rule optimization algorithm for subjective interest degree on massive data sets. The present invention uses compound templates to optimize analysis at the same time, namely, it is divided into general impression knowledge template (GI) and relatively accurate knowledge template (RPC). This classification expands the meaning of users The range of expression helps to optimize the association rules from different focuses. In addition, the role of restriction and inclusion templates is reflected in different degrees of interest, and the degree of interest is refined into four types, including consistency, consequential The degree of predictability, the degree of unpredictability of antecedents, and the degree of unpredictability make the optimization granularity very clear; the optimization of the interesting degree calculation model combined with the composite template makes the calculation of the interesting degree reasonably suitable for the complex analysis environment.

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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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00
Inventor 牛新征周冬梅侯孟书杨健
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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