Horn axiomatizations for sequential data

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...

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Detalles Bibliográficos
Autores: Balcázar Navarro, José Luis|||0000-0003-4248-4528, Casas Garriga, Gemma
Tipo de recurso: informe técnico
Fecha de publicación:2005
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/85639
Acceso en línea:https://hdl.handle.net/2117/85639
Access Level:acceso abierto
Palabra clave:Deterministic association rules
Formal concept analysis
Sequential data
Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica
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spelling Horn axiomatizations for sequential dataBalcázar Navarro, José Luis|||0000-0003-4248-4528Casas Garriga, GemmaDeterministic association rulesFormal concept analysisSequential dataÀrees temàtiques de la UPC::Informàtica::Informàtica teòricaWe 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 of 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.20052005-09-0120162016-04-14reporthttp://purl.org/coar/resource_type/c_93fcVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/reportapplication/postscripthttps://hdl.handle.net/2117/85639reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/856392026-05-27T15:37:01Z
dc.title.none.fl_str_mv Horn axiomatizations for sequential data
title Horn axiomatizations for sequential data
spellingShingle Horn axiomatizations for sequential data
Balcázar Navarro, José Luis|||0000-0003-4248-4528
Deterministic association rules
Formal concept analysis
Sequential data
Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica
title_short Horn axiomatizations for sequential data
title_full Horn axiomatizations for sequential data
title_fullStr Horn axiomatizations for sequential data
title_full_unstemmed Horn axiomatizations for sequential data
title_sort Horn axiomatizations for sequential data
dc.creator.none.fl_str_mv Balcázar Navarro, José Luis|||0000-0003-4248-4528
Casas Garriga, Gemma
author Balcázar Navarro, José Luis|||0000-0003-4248-4528
author_facet Balcázar Navarro, José Luis|||0000-0003-4248-4528
Casas Garriga, Gemma
author_role author
author2 Casas Garriga, Gemma
author2_role author
dc.subject.none.fl_str_mv Deterministic association rules
Formal concept analysis
Sequential data
Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica
topic Deterministic association rules
Formal concept analysis
Sequential data
Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica
description 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 of 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.
publishDate 2005
dc.date.none.fl_str_mv 2005
2005-09-01
2016
2016-04-14
dc.type.none.fl_str_mv report
http://purl.org/coar/resource_type/c_93fc
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/85639
url https://hdl.handle.net/2117/85639
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/postscript
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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