Enhanced approximation of the emerging pattern space using an incremental approach

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 cons...

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Detalles Bibliográficos
Autores principales: Grandinetti, Walter M., Chesñevar, Carlos Iván, Falappa, Marcelo Alejandro
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2005
Materias:
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
pattern mining
emerging patterns
maximal patterns
incremental mining
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/21157
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-21157
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
pattern mining
emerging patterns
maximal patterns
incremental mining
spellingShingle Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
pattern mining
emerging patterns
maximal patterns
incremental mining
Grandinetti, Walter M.
Chesñevar, Carlos Iván
Falappa, Marcelo Alejandro
Enhanced approximation of the emerging pattern space using an incremental approach
topic_facet Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
pattern mining
emerging patterns
maximal patterns
incremental mining
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.
format Objeto de conferencia
Objeto de conferencia
author Grandinetti, Walter M.
Chesñevar, Carlos Iván
Falappa, Marcelo Alejandro
author_facet Grandinetti, Walter M.
Chesñevar, Carlos Iván
Falappa, Marcelo Alejandro
author_sort Grandinetti, Walter M.
title Enhanced approximation of the emerging pattern space using an incremental approach
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
publishDate 2005
url http://sedici.unlp.edu.ar/handle/10915/21157
work_keys_str_mv AT grandinettiwalterm enhancedapproximationoftheemergingpatternspaceusinganincrementalapproach
AT chesnevarcarlosivan enhancedapproximationoftheemergingpatternspaceusinganincrementalapproach
AT falappamarceloalejandro enhancedapproximationoftheemergingpatternspaceusinganincrementalapproach
bdutipo_str Repositorios
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