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 | Acesso ao texto completo restrito à biblioteca da Embrapa Cerrados. Para informações adicionais entre em contato com cpac.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
18/10/2001 |
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Data da última atualização: |
18/10/2001 |
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Autoria: |
PRADO, H. A. do; HIRTLE, S. C.; ENGEL, P. M. |
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Título: |
Clustering algorithms for data mining. |
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Ano de publicação: |
2000 |
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Fonte/Imprenta: |
Revista Tecnologia da Informacao, Brasilia, v.2, n.1, p.51-58, 2000. |
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Idioma: |
Inglês |
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Conteúdo: |
Knowledge Discovery from Databases (KDD) can be seen as a set of computer-aided Knowledge discovery techniques. Research in this field has heen carried out according to two main dimension: simplifying and scaling of the whole process to cope with very large databases. The first efforts of KDD research community addressed strongly the task of prediction, e.g., regression and classification. The aim of this task is to fit a model over a data set for which it is Known some interesting feature in order to predict the same feature for a new case. Recently, researchers have focussed the task of description that includes clustering and finding associative rules. In using the descriptive approach one is interested in figures out how a set of objects are organized in the space of their dimensions. This paper presents a study on clustering methods focussing, in particular, those that have been the target of research efforts in the KDD realm. Our objective is to state a well-founded basis for forthcoming work.
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Palavras-Chave: |
Clustering; Data mining; Descoberta de conhecimento; Knowledge discovery; Mineracao de dados; Unsupervised learning. |
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Thesagro: |
Base de Dados. |
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Thesaurus NAL: |
databases. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01669naa a2200241 a 4500
001 1555548
005 2001-10-18
008 2000 bl --- 0-- u #d
100 1 $aPRADO, H. A. do
245 $aClustering algorithms for data mining.
260 $c2000
520 $aKnowledge Discovery from Databases (KDD) can be seen as a set of computer-aided Knowledge discovery techniques. Research in this field has heen carried out according to two main dimension: simplifying and scaling of the whole process to cope with very large databases. The first efforts of KDD research community addressed strongly the task of prediction, e.g., regression and classification. The aim of this task is to fit a model over a data set for which it is Known some interesting feature in order to predict the same feature for a new case. Recently, researchers have focussed the task of description that includes clustering and finding associative rules. In using the descriptive approach one is interested in figures out how a set of objects are organized in the space of their dimensions. This paper presents a study on clustering methods focussing, in particular, those that have been the target of research efforts in the KDD realm. Our objective is to state a well-founded basis for forthcoming work.
650 $adatabases
650 $aBase de Dados
653 $aClustering
653 $aData mining
653 $aDescoberta de conhecimento
653 $aKnowledge discovery
653 $aMineracao de dados
653 $aUnsupervised learning
700 1 $aHIRTLE, S. C.
700 1 $aENGEL, P. M.
773 $tRevista Tecnologia da Informacao, Brasilia$gv.2, n.1, p.51-58, 2000.
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