A Unified Hierarchy for Functional Dependencies, Conditional Functional Dependencies and Association Rules

2009 ◽  
pp. 98-113 ◽  
Author(s):  
Raoul Medina ◽  
Lhouari Nourine
Keyword(s):  
Association Rules ◽  
Functional Dependencies

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).

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.

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).

Mining Rare Association Rules by Discovering Quasi-Functional Dependencies

2010 ◽  
pp. 131-149
Author(s):  
Giulia Bruno ◽  
Paolo Garza ◽  
Elisa Quintarelli
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Incremental Algorithm ◽  
Functional Dependencies ◽  
Data Mining Technique ◽  
Data Set ◽  
Rare Association ◽  
Normal Behavior ◽  
The Impact ◽  
Over Time

In the context of anomaly detection, the data mining technique of extracting association rules can be used to identify rare rules which represent infrequent situations. A method to detect rare rules is to first infer the normal behavior of objects in the form of quasi-functional dependencies (i.e. functional dependencies that frequently hold), and then analyzing rare violations with respect to them. The quasi-functional dependencies are usually inferred from the current instance of a database. However, in several applications, the database is not static, but new data are added or deleted continuously. Thus, the anomalies have to be updated because they change over time. In this chapter, we propose an incremental algorithm to efficiently maintain up-to-date rules (i.e., functional and quasi-functional dependencies). The impact of the cardinality of the data set and the number of new tuples on the execution time is evaluated through a set of experiments on synthetic and real databases, whose results are here reported.

Modified algorithm for fast bandwidth selection for kernel estimates of multidimensional probability densities

2020 ◽  
pp. 9-13
Author(s):  
A. V. Lapko ◽  
V. A. Lapko
Keyword(s):  
Probability Density ◽  
Optimal Parameter ◽  
Random Variables ◽  
Kernel Functions ◽  
Independent Random Variables ◽  
Functional Dependencies ◽  
Approximation Properties ◽  
Probability Density Estimation ◽  
Multidimensional Probability ◽  
Selection Of

An original technique has been justified for the fast bandwidths selection of kernel functions in a nonparametric estimate of the multidimensional probability density of the Rosenblatt–Parzen type. The proposed method makes it possible to significantly increase the computational efficiency of the optimization procedure for kernel probability density estimates in the conditions of large-volume statistical data in comparison with traditional approaches. The basis of the proposed approach is the analysis of the optimal parameter formula for the bandwidths of a multidimensional kernel probability density estimate. Dependencies between the nonlinear functional on the probability density and its derivatives up to the second order inclusive of the antikurtosis coefficients of random variables are found. The bandwidths for each random variable are represented as the product of an undefined parameter and their mean square deviation. The influence of the error in restoring the established functional dependencies on the approximation properties of the kernel probability density estimation is determined. The obtained results are implemented as a method of synthesis and analysis of a fast bandwidths selection of the kernel estimation of the two-dimensional probability density of independent random variables. This method uses data on the quantitative characteristics of a family of lognormal distribution laws.

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