A New Interestingness Measure of Association Rules

2008 ◽  
Author(s):  
Jianhua Liu ◽  
Xiaoping Fan ◽  
Zhihua Qu
Keyword(s):  
Association Rules ◽  
Interestingness Measure ◽  
Measure Of Association

scholarly journals A novel interestingness measure based on fusion model for association rules mining

2021 ◽  
Vol 336 ◽  
pp. 05009
Author(s):  
Junrui Yang ◽  
Lin Xu
Keyword(s):  
Association Rules ◽  
Negative Association ◽  
Fusion Model ◽  
Interestingness Measures ◽  
Association Rules Mining ◽  
Interestingness Measure ◽  
Advantages And Disadvantages ◽  
Measure Model ◽  
Negative Association Rules ◽  
The Difference

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.

scholarly journals Toward a Theory of Normalizing Function of Interestingness Measure of Binary Association Rules

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Armand Armand ◽  
André Totohasina ◽  
Daniel Rajaonasy Feno
Keyword(s):  
Association Rules ◽  
Quality Measures ◽  
Measures Of Association ◽  
Interestingness Measures ◽  
Association Rules Mining ◽  
Interestingness Measure ◽  
Normalization Function

Regarding the existence of more than sixty interestingness measures proposed in the literature since 1993 till today in the topics of association rules mining and facing the importance these last one, the research on normalization probabilistic quality measures of association rules has already led to many tangible results to consolidate the various existing measures in the literature. This article recommends a simple way to perform this normalization. In the interest of a unified presentation, the article offers also a new concept of normalization function as an effective tool for resolution of the problem of normalization measures that have already their own normalization functions.

scholarly journals An Improved Evaluation Methodology for Mining Association Rules

Axioms ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 17
Author(s):  
Fuguang Bao ◽  
Linghao Mao ◽  
Yiling Zhu ◽  
Cancan Xiao ◽  
Chonghuan Xu
Keyword(s):  
Risk Analysis ◽  
Association Rules ◽  
Evaluation Methodology ◽  
Personalized Recommendation ◽  
Practical Application ◽  
Chi Square ◽  
Objective Criterion ◽  
Interestingness Measure ◽  
Advantages And Disadvantages ◽  
Chi Square Analysis

At present, association rules have been widely used in prediction, personalized recommendation, risk analysis and other fields. However, it has been pointed out that the traditional framework to evaluate association rules, based on Support and Confidence as measures of importance and accuracy, has several drawbacks. Some papers presented several new evaluation methods; the most typical methods are Lift, Improvement, Validity, Conviction, Chi-square analysis, etc. Here, this paper first analyzes the advantages and disadvantages of common measurement indicators of association rules and then puts forward four new measure indicators (i.e., Bi-support, Bi-lift, Bi-improvement, and Bi-confidence) based on the analysis. At last, this paper proposes a novel Bi-directional interestingness measure framework to improve the traditional one. In conclusion, the bi-directional interestingness measure framework (Bi-support and Bi-confidence framework) is superior to the traditional ones in the aspects of the objective criterion, comprehensive definition, and practical application.

scholarly journals Using the interestingness measure lift to generate association rules

2015 ◽  
Vol 4 (1) ◽  
pp. 156 ◽  
Author(s):  
Nada Hussein ◽  
Abdallah Alashqur ◽  
Bilal Sowan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Search Space ◽  
The Other ◽  
Frequent Patterns ◽  
New Approach ◽  
Left Hand ◽  
Interestingness Measure ◽  
The Right

<p>In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for finding frequent patterns and confidence for finding association rules. A shortcoming of confidence is that it does not capture the correlation that exists between the left-hand side (LHS) and the right-hand side (RHS) of an association rule. On the other hand, the interestingness measure lift captures such as correlation in the sense that it tells us whether the LHS influences the RHS positively or negatively. Therefore, using Lift instead of confidence as a criteria for discovering association rules can be more effective. It also gives the user more choices in determining the kind of association rules to be discovered. This in turn helps to narrow down the search space and consequently, improves performance. In this paper, we describe a new approach for discovering association rules that is based on Lift and not based on confidence.</p>

A Kernel Density Estimation Based Interestingness Measure for Association Rule Mining

2010 ◽  
Vol 20-23 ◽  
pp. 389-394
Author(s):  
Zhi Feng Hao ◽  
Rui Chu Cai ◽  
Tang Wu ◽  
Yi Yuan Zhou
Keyword(s):  
Density Estimation ◽  
Association Rules ◽  
Kernel Density Estimation ◽  
Association Rule ◽  
Association Rule Mining ◽  
Kernel Density ◽  
Rule Mining ◽  
Data Set ◽  
Interestingness Measures ◽  
Interestingness Measure

Association rules provide a concise statement of potentially useful information, and have been widely used in real applications. However, the usefulness of association rules highly depends on the interestingness measure which is used to select interesting rules from millions of candidates. In this study, a probability analysis of association rules is conducted, and a discrete kernel density estimation based interestingness measure is proposed accordingly. The new proposed interestingness measure makes the most of the information contained in the data set and obtains much lower falsely discovery rate than the existing interestingness measures. Experimental results show the effectiveness of the proposed interestingness measure.

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