Treffer: Horn axiomatizations for sequential data
Title:
Horn axiomatizations for sequential data
Authors:
Source:
Database theoryTheoretical computer science. 371(3):247-264
Publisher Information:
Amsterdam: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 62 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Systèmes d'information. Bases de données, Information systems. Data bases, Algorithme, Algorithm, Algoritmo, Informatique théorique, Computer theory, Informática teórica, Modèle réticulaire, Lattice model, Modelo reticular, Opérateur fermeture, Closure operator, Ordre approximation, Approximation order, Orden aproximación, Axiomatisation, Modèle séquentiel, Sequential pattern, Proposition Horn, Règle association, Association rule, Association rules, Closure operators, Propositional Horn theories, Sequential patterns
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Departament de Llenguatges i Sistemes Informàtics, Laboratori d'Algorismica Relacional. Complexitat i Aprenentatge, Universitat Politècnica de Catalunya, Barcelona, Spain
ISSN:
0304-3975
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18568768
Database:
PASCAL Archive
Weitere Informationen
We propose a notion of deterministic association rules for ordered data. We prove that our proposed rules can be formally justified by a purely logical characterization, namely, a natural notion of empirical Horn approximation for ordered data which involves background Horn conditions; these ensure the consistency of the propositional theory obtained with the ordered context. The whole framework resorts to concept lattice models from Formal Concept Analysis, but adapted to ordered contexts. We also discuss a general method to mine these rules that can be easily incorporated into any algorithm for mining closed sequences, of which there are already some in the literature.