Enhanced approximation of the emerging pattern space using an incremental approach

Autores
Grandinetti, Walter M.; Chesñevar, Carlos Iván; Falappa, Marcelo Alejandro
Año de publicación
2005
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
From the many different patterns that can be extracted from data, so-called emerging patterns (EPs) are a particular useful kind. EPs are itemsets whose supports increase significantly from one dataset to another. Existing methods used to discover EPs have been successfully applied only under a constrained search space. Although they may provide a very efficient way of discovering some sort of EPs, they are rather limited when the whole set of EPs is needed, as they just compute an approximation of that set. Recent EPs techniques rely on borders, a concise representation of the candidate itemsets which does not require computing an exponentially large number of such candidates. In this paper we outline a new method which exploits previously mined data using an incremental approach, requiring thus less dataset accesses. Our proposal also aims to reduce the amount of work needed to perform difference operations among borders taking into account special properties of the itemsets.
Eje: Inteligencia artificial
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
pattern mining
emerging patterns
maximal patterns
incremental mining
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/21157

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network_name_str SEDICI (UNLP)
spelling Enhanced approximation of the emerging pattern space using an incremental approachGrandinetti, Walter M.Chesñevar, Carlos IvánFalappa, Marcelo AlejandroCiencias InformáticasARTIFICIAL INTELLIGENCEpattern miningemerging patternsmaximal patternsincremental miningFrom the many different patterns that can be extracted from data, so-called emerging patterns (EPs) are a particular useful kind. EPs are itemsets whose supports increase significantly from one dataset to another. Existing methods used to discover EPs have been successfully applied only under a constrained search space. Although they may provide a very efficient way of discovering some sort of EPs, they are rather limited when the whole set of EPs is needed, as they just compute an approximation of that set. Recent EPs techniques rely on borders, a concise representation of the candidate itemsets which does not require computing an exponentially large number of such candidates. In this paper we outline a new method which exploits previously mined data using an incremental approach, requiring thus less dataset accesses. Our proposal also aims to reduce the amount of work needed to perform difference operations among borders taking into account special properties of the itemsets.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI)2005-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf263-267http://sedici.unlp.edu.ar/handle/10915/21157enginfo:eu-repo/semantics/altIdentifier/isbn/950-665-337-2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:54:32Zoai:sedici.unlp.edu.ar:10915/21157Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:32.362SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Enhanced approximation of the emerging pattern space using an incremental approach
title Enhanced approximation of the emerging pattern space using an incremental approach
spellingShingle Enhanced approximation of the emerging pattern space using an incremental approach
Grandinetti, Walter M.
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
pattern mining
emerging patterns
maximal patterns
incremental mining
title_short Enhanced approximation of the emerging pattern space using an incremental approach
title_full Enhanced approximation of the emerging pattern space using an incremental approach
title_fullStr Enhanced approximation of the emerging pattern space using an incremental approach
title_full_unstemmed Enhanced approximation of the emerging pattern space using an incremental approach
title_sort Enhanced approximation of the emerging pattern space using an incremental approach
dc.creator.none.fl_str_mv Grandinetti, Walter M.
Chesñevar, Carlos Iván
Falappa, Marcelo Alejandro
author Grandinetti, Walter M.
author_facet Grandinetti, Walter M.
Chesñevar, Carlos Iván
Falappa, Marcelo Alejandro
author_role author
author2 Chesñevar, Carlos Iván
Falappa, Marcelo Alejandro
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
pattern mining
emerging patterns
maximal patterns
incremental mining
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
pattern mining
emerging patterns
maximal patterns
incremental mining
dc.description.none.fl_txt_mv From the many different patterns that can be extracted from data, so-called emerging patterns (EPs) are a particular useful kind. EPs are itemsets whose supports increase significantly from one dataset to another. Existing methods used to discover EPs have been successfully applied only under a constrained search space. Although they may provide a very efficient way of discovering some sort of EPs, they are rather limited when the whole set of EPs is needed, as they just compute an approximation of that set. Recent EPs techniques rely on borders, a concise representation of the candidate itemsets which does not require computing an exponentially large number of such candidates. In this paper we outline a new method which exploits previously mined data using an incremental approach, requiring thus less dataset accesses. Our proposal also aims to reduce the amount of work needed to perform difference operations among borders taking into account special properties of the itemsets.
Eje: Inteligencia artificial
Red de Universidades con Carreras en Informática (RedUNCI)
description From the many different patterns that can be extracted from data, so-called emerging patterns (EPs) are a particular useful kind. EPs are itemsets whose supports increase significantly from one dataset to another. Existing methods used to discover EPs have been successfully applied only under a constrained search space. Although they may provide a very efficient way of discovering some sort of EPs, they are rather limited when the whole set of EPs is needed, as they just compute an approximation of that set. Recent EPs techniques rely on borders, a concise representation of the candidate itemsets which does not require computing an exponentially large number of such candidates. In this paper we outline a new method which exploits previously mined data using an incremental approach, requiring thus less dataset accesses. Our proposal also aims to reduce the amount of work needed to perform difference operations among borders taking into account special properties of the itemsets.
publishDate 2005
dc.date.none.fl_str_mv 2005-05
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info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_5794
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/950-665-337-2
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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