Data/hora: 15/06/2024 08:10:14

Biblioteca(s): | Embrapa Cerrados.
Data corrente: | 18/10/2001
Data da última atualização: | 18/10/2001
Autoria: | PRADO, H. A. do; HIRTLE, S. C.; ENGEL, P. M.
Título: | Clustering algorithms for data mining.
Ano de publicação: | 2000
Fonte/Imprenta: | Revista Tecnologia da Informacao, Brasilia, v.2, n.1, p.51-58, 2000.
Idioma: | Inglês
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.
Palavras-Chave: | Clustering; Data mining; Descoberta de conhecimento; Knowledge discovery; Mineracao de dados; Unsupervised learning.
Thesagro: | Base de Dados.
Thesaurus Nal: | databases.
Categoria do assunto: | --
Registro original: | Embrapa Cerrados (CPAC)
Exemplares: | CPAC - (12376-1) - UPC - SP - -- - CRI5539 -  - CRI5539 - DIS