Dynamic Relation-Based Analysis of Objective Interestingness Measures in Association Rules Mining

2018 ◽  
pp. 38-46
Author(s):  
Rachasak Somyanonthanakul ◽  
Thannaruk Theeramunkong
Keyword(s):  
Association Rules ◽  
Interestingness Measures ◽  
Association Rules Mining ◽  
Dynamic Relation

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 A Novel Method of Interestingness Measures for Association Rules Mining Based on Profit

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chunhua Ju ◽  
Fuguang Bao ◽  
Chonghuan Xu ◽  
Xiaokang Fu
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Negative Relationship ◽  
Practical Application ◽  
Objective Criterion ◽  
Interestingness Measures ◽  
Association Rules Mining ◽  
New Methods ◽  
Measure Methods ◽  
Novel Method

Association rules mining is an important topic in the domain of data mining and knowledge discovering. Some papers have presented several interestingness measure methods; the most typical areSupport,Confidence,Lift,Improve, and so forth. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative relationship, and so forth. This paper proposes three new methods,Bi-lift, Bi-improve, andBi-confidence, forLift, Improve, and Confidence, respectively. Then, on the basis of utility function and the executing cost of rules, we propose interestingness function based on profit (IFBP) considering subjective preferences and characteristics of specific application object. Finally, a novel measure framework is proposed to improve the traditional one through experimental analysis. In conclusion, the new methods and measure framework are prior to the traditional ones in the aspects of objective criterion, comprehensive definition, and practical application.

Using Association Rules Mining to Characterize of Loss of Control Eating in Childhood

Appetite ◽  
2021 ◽  
pp. 105236
Author(s):  
Alaina L. Pearce ◽  
Timothy R. Brick ◽  
Travis Masterson ◽  
Shana Adise ◽  
S. Nicole Fearnbach ◽  
...  
Keyword(s):  
Association Rules ◽  
Loss Of Control ◽  
Loss Of Control Eating ◽  
Association Rules Mining
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