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New probabilistic interest measures for association rules [article]

Michael Hahsler, Kurt Hornik
2008 arXiv   pre-print
Based on the probabilistic framework we develop two new interest measures, hyper-lift and hyper-confidence, which can be used to filter or order mined association rules.  ...  Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules.  ...  New measures of interest In the simple probabilistic model all items as well as combinations of items occur following independent Poisson processes.  ... 
arXiv:0803.0966v1 fatcat:6tg35fqknvbkvftpypwytkaugi

New probabilistic interest measures for association rules

Michael Hahsler, Kurt Hornik
2007 Intelligent Data Analysis  
Based on the probabilistic framework we develop two new interest measures, hyper-lift and hyper-confidence, which can be used to filter or order mined association rules.  ...  Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules.  ...  New measures of interest In the simple probabilistic model all items as well as combinations of items occur following independent Poisson processes.  ... 
doi:10.3233/ida-2007-11502 fatcat:6uavsqyhlzhxxfqs7vxd43pd3u

Probabilistic Measures for Interestingness of Deviations - A Survey

Adnan Masood, Sofiane Ouaguenouni
2013 International Journal of Artificial Intelligence & Applications  
The most common approach is to employ measures of rule interestingness to filter the results of the association rule generation process.  ...  In this brief survey, we review the current state of literature around interestingness of deviations, i.e. outliers with specific interest around probabilistic measures using Bayesian belief networks.  ...  Geng then reviewed 38 objective, 3 subjective, and 2 semantic interestingness measures for association/classification rules according to the nine interestingness criteria.  ... 
doi:10.5121/ijaia.2013.4201 fatcat:3bwuzx3egbc7xk62g3zosizrcu

A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link [chapter]

Israel-César Lerman, Jérôme Azé
2007 Studies in Computational Intelligence  
The second corresponds to a discriminant extension of the obtained probabilistic index for measuring an association rule in the context of a relevant set of association rules.  ...  The interestingness measures for pattern associations proposed in the data mining literature depend only on the observation of relative frequencies obtained from 2 × 2 contingency tables.  ...  A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link 1 Introduction Seeking for a relevant interestingness measure in the context of a given data base  ... 
doi:10.1007/978-3-540-44918-8_9 fatcat:3ihhxwycgvgdvmui65cbcn7qh4

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

Armand Armand, André Totohasina, Daniel Rajaonasy Feno
2018 International Journal of Mathematics and Mathematical Sciences  
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.  ...  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  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2018/4814716 fatcat:oqbet65g7ffjvcfkfbha6one6y

Rules for Inducing Hierarchies from Social Tagging Data [chapter]

Hang Dong, Wei Wang, Frans Coenen
2018 Lecture Notes in Computer Science  
We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce  ...  We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.  ...  We also proposed another new rule called probabilistic association, which has a strong probabilistic foundation. We use R1 to R5 to refer to these rules.  ... 
doi:10.1007/978-3-319-78105-1_38 fatcat:jifgrag63bfhrcqv7cnvhmlsca

Fuzzy classification rule mining based on Genetic Network Programming algorithm

Karla Taboada, Shingo Mabu, Eloy Gonzales, Kaoru Shimada, Kotaro Hirasawa
2009 2009 IEEE International Conference on Systems, Man and Cybernetics  
However, the current studies show that the association rule-based classifiers may also suffer some problems inherited from association rule mining such as handling of (1) continuous data and (2) the support  ...  Association rule-based classification is one of the most important data mining techniques applied to many scientific problems.  ...  Fig. 3 . 3 Basic GNP structure for fuzzy association rule mining. Fig. 4 . 4 Probabilistic transition from one judgment node to another.  ... 
doi:10.1109/icsmc.2009.5346640 dblp:conf/smc/TaboadaMGKH09 fatcat:dsu4ktqwa5bv5nfboqtu7globm

Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis

Henning Müller, Thierry Pun, David Squire
2004 International Journal of Computer Vision  
Association rules can be derived from these data. A sort introduction of association rules is given in Section 3.1. without going into details.  ...  This study uses images marked together in the same relevance feedback step for the calculation of a new feature weight.  ...  We are not necessarily interested in the association rules that occur the most frequently.  ... 
doi:10.1023/b:visi.0000004832.02269.45 fatcat:b2la4zdi2jbptmd3zx36pr24oq

Prediction Interval Adjustment for Load-Forecasting using Machine Learning

Miguel A. Zuniga-Garcia, G. Santamaría-Bonfil, G. Arroyo-Figueroa, Rafael Batres
2019 Applied Sciences  
In this paper, we develop a probabilistic load-forecasting method based on Association Rules and Artificial Neural Networks for Short-Term Load Forecasting (2 h ahead).  ...  Next, association rules are employed to adjust the prediction intervals by exploiting the confidence and support of the association rules.  ...  Energy Sustainability (Agreement: S0019201401) Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9245269 fatcat:pz5o2iobojhhxhz2cnp2ta4age

On modeling of if-then rules for probabilistic inference

Hung T. Nguyen, I. R. Goodman
1994 International Journal of Intelligent Systems  
In discussing Bayesian updating procedure and belief function construction, we provide a new method for modeling if ... then rules as Boolean elements, and yet, compatible with conditional probability  ...  We identify various situations in probabilistic intelligent systems in which conditionals (rules) as mathematical entities as well as their conditional logic operations are needed.  ...  Walker for their constructive discussions concerning this work.  ... 
doi:10.1002/int.4550090406 fatcat:wdft2y3klbaezodh3ctmand3yy

Evidential Confirmation as Transformed Probability [article]

Benjamin N. Grosof
2013 arXiv   pre-print
A considerable body of work in AI has been concerned with aggregating measures of confirmatory and disconfirmatory evidence for a common set of propositions.  ...  Claiming classical probability to be inadequate or inappropriate, several researchers have gone so far as to invent new formalisms and methods.  ...  Thanks also to Richard Duda, Peter Hart, and Nils Nilsson for their interest, and their help in providing the unpublished history of the development of the PROSPECTOR method.  ... 
arXiv:1304.3439v1 fatcat:2md7qvuxknbkvaqvdsecgexqam

Page 10366 of Mathematical Reviews Vol. , Issue 2004m [page]

2004 Mathematical Reviews  
Also, they argue that the quantum probabilistic rule should not be put in contrast to the classical probabilistic rule.  ...  The authors discuss two rules of addition of probabilities: the quantum probabilistic rule P = P; + P2 +2cos@\/ PP, and the classical probabilistic rule P = P; + P2, where P; and P> represent mutually  ... 

Reasoning with imprecise probabilities

Andrés Cano, Fabio G. Cozman, Thomas Lukasiewicz
2007 International Journal of Approximate Reasoning  
Acknowledgments We would like to thank Teddy Seidenfeld and Robert Nau for their effort in handling the papers at ISIPTA'05, and Marco Zaffalon and Thierry Denoeux for crucial support at various stages  ...  Moreover, it presents new algorithms and complexity results for probabilistic inference in these logics.  ...  The paper addresses the problem of efficiently computing the conservative updating rule for robust classification with Bayesian networks.  ... 
doi:10.1016/j.ijar.2006.09.001 fatcat:3ijlksgsljhuvi2a4jdhoxspqa

An Extension of Totohasina's Normalization Theory of Quality Measures of Association Rules

Armand, André Totohasina, Daniel Rajaonasy Feno
2019 International Journal of Mathematics and Mathematical Sciences  
In the context of binary data mining, for unifying view on probabilistic quality measures of association rules, Totohasina's theory of normalization of quality measures of association rules primarily based  ...  This paper presents an extension of it, as a new normalization method based on proper homographic homeomorphism that appears most consequent.  ...  International Journal of Mathematics and Mathematical Sciences 7 Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2019/7829805 fatcat:bt3qnsvawncpdiml5a65swvidq

MINING DEPENDENT PATTERNS IN PROBABILISTIC DATABASES

SHICHAO ZHANG, CHENGQI ZHANG, JEFFREY XU YU
2004 Cybernetics and systems  
This paper designs a new strategy for identifying potentially useful patterns in probabilistic databases.  ...  In order to implement interesting association analysis, a wide range of problems has been investigated over such diverse topics as models for discovering 30 generalized association rules (Srikant and  ...  The authors would like to thank the reviewers for their detailed constructive comments on the first version of this paper.  ... 
doi:10.1080/01969720496443390 fatcat:v6l2vfkgfjbdzd3bwxdmdig53y
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