Approximate Functional Dependencies Mining Using Association Rules Specificity Interestingness Measure

2016 ◽  
Vol 15 (5) ◽  
pp. 1-10 ◽  
Author(s):  
Jalal Atoum
Keyword(s):  
Association Rules ◽  
Functional Dependencies ◽  
Interestingness Measure

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.

Semi-Automatic Ontology Construction by Exploiting Functional Dependencies and Association Rules

Semantic Web ◽  
2013 ◽  
pp. 76-96
Author(s):  
Luca Cagliero ◽  
Tania Cerquitelli ◽  
Paolo Garza
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Description Logic ◽  
Functional Dependency ◽  
Functional Dependencies ◽  
Rule Mining ◽  
Domain Experts ◽  
Data Schema ◽  
Input Dataset

This paper presents a novel semi-automatic approach to construct conceptual ontologies over structured data by exploiting both the schema and content of the input dataset. It effectively combines two well-founded database and data mining techniques, i.e., functional dependency discovery and association rule mining, to support domain experts in the construction of meaningful ontologies, tailored to the analyzed data, by using Description Logic (DL). To this aim, functional dependencies are first discovered to highlight valuable conceptual relationships among attributes of the data schema (i.e., among concepts). The set of discovered correlations effectively support analysts in the assertion of the Tbox ontological statements (i.e., the statements involving shared data conceptualizations and their relationships). Then, the analyst-validated dependencies are exploited to drive the association rule mining process. Association rules represent relevant and hidden correlations among data content and they are used to provide valuable knowledge at the instance level. The pushing of functional dependency constraints into the rule mining process allows analysts to look into and exploit only the most significant data item recurrences in the assertion of the Abox ontological statements (i.e., the statements involving concept instances and their relationships).

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.

Association Rules Extraction for the Identification of Functional Dependencies in Complex Technical Infrastructures

2020 ◽  
pp. 107305
Author(s):  
Federico Antonello ◽  
Piero Baraldi ◽  
Ahmed Shokry ◽  
Enrico Zio ◽  
U. Gentile ◽  
...  
Keyword(s):  
Association Rules ◽  
Functional Dependencies ◽  
Rules Extraction

scholarly journals The Hierarchies of Multivalued Attribute Domains and Corresponding Applications in Data Mining

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yuxia Lei ◽  
Yushu Yan ◽  
Yonghua Han ◽  
Feng Jiang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Language Processing ◽  
Common Knowledge ◽  
Formal Concept ◽  
Research Association ◽  
Functional Dependencies ◽  
Mobile Data ◽  
Healthcare Data ◽  
Machine Learning Models

In mobile computing, machine learning models for natural language processing (NLP) have become one of the most attractive focus areas in research. Association rules among attributes are common knowledge patterns, which can often provide potential and useful information such as mobile users' interests. Actually, almost each attribute is associated with a hierarchy of the domain. Given an relation R=(U,A) and any cut αa on the hierarchy for every attribute a, there is another rough relation RΦ, where Φ=(αa:a∈A). This paper will establish the connection between the functional dependencies in R and RΦ, propose the method for extracting reducts in RΦ, and demonstrate the implementation of proposed method on an application in data mining of association rules. The method for acquiring association rules consists of the following three steps: (1) translating natural texts into relations, by NLP; (2) translating relations into rough ones, by attributes analysis or fuzzy k-means (FKM) clustering; and (3) extracting association rules from concept lattices, by formal concept analysis (FCA). Our experimental results show that the proposed methods, which can be applied directly to regular mobile data such as healthcare data, improved quality, and relevance of rules.

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>

Semi-Automatic Ontology Construction by Exploiting Functional Dependencies and Association Rules

2011 ◽  
Vol 7 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Luca Cagliero ◽  
Tania Cerquitelli ◽  
Paolo Garza
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Description Logic ◽  
Functional Dependency ◽  
Functional Dependencies ◽  
Rule Mining ◽  
Data Schema ◽  
Input Dataset ◽  
Data Content

This paper presents a novel semi-automatic approach to construct conceptual ontologies over structured data by exploiting both the schema and content of the input dataset. It effectively combines two well-founded database and data mining techniques, i.e., functional dependency discovery and association rule mining, to support domain experts in the construction of meaningful ontologies, tailored to the analyzed data, by using Description Logic (DL). To this aim, functional dependencies are first discovered to highlight valuable conceptual relationships among attributes of the data schema (i.e., among concepts). The set of discovered correlations effectively support analysts in the assertion of the Tbox ontological statements (i.e., the statements involving shared data conceptualizations and their relationships). Then, the analyst-validated dependencies are exploited to drive the association rule mining process. Association rules represent relevant and hidden correlations among data content and they are used to provide valuable knowledge at the instance level. The pushing of functional dependency constraints into the rule mining process allows analysts to look into and exploit only the most significant data item recurrences in the assertion of the Abox ontological statements (i.e., the statements involving concept instances and their relationships).

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