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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/21157
Ver los metadatos del registro completo
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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 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
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http://sedici.unlp.edu.ar/handle/10915/21157 |
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dc.language.none.fl_str_mv |
eng |
language |
eng |
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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) |
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openAccess |
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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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