Document
type: Class

Continuant
type: Class

Entity
type: Class

Generically Dependent Continuant
type: Class

Proceedings - IEEE International Conference on Data Mining, ICDM
type: Thing
type: Document
publication venue for: A clustering method for very large mixed data sets
type: Book
type: Entity
type: Proceedings
type: Continuant
type: Generically Dependent Continuant
type: Information Content Entity

RDF description of A clustering method for very large mixed data sets - http://orbis.uaslp.mx/vivo/individual/scopus8499
rights: termsOfUse
date: 2026-08-27T13:00:15
publisher: vivo
type: Document

Article
type: Class

A clustering method for very large mixed data sets
type: Generically Dependent Continuant
date time value: conference paper date 8500
most specific type: Document
related by: authorship_55020
type: Conference Paper
type: Entity
origen: scopus
page start: 643
related by: authorship_55019
estado: published
type: Document
type: Continuant
type: Information Content Entity
abstract: In the developed countries, especially over the last decade, there has been an explosive growth in the capability to generate, collect and use very large data sets. The objects of these data sets could be simultaneously described by quantitative and qualitative attributes. At present, algorithm able to process either very large data sets (in metric spaces) or mixed (qualitative and quantitative) incomplete data (missing value) sets have been developed, but not for very large mixed incomplete data sets. In this paper we introduce a new clustering method named GLC%2b to process very large niixed incomplete data sets in order to obtain a partition in connected sets. © 2001 IEEE.
most specific type: Conference Paper
type: Thing
type: Article
abstract: In the developed countries, especially over the last decade, there has been an explosive growth in the capability to generate, collect and use very large data sets. The objects of these data sets could be simultaneously described by quantitative and qualitative attributes. At present, algorithm able to process either very large data sets (in metric spaces) or mixed (qualitative and quantitative) incomplete data (missing value) sets have been developed, but not for very large mixed incomplete data sets. In this paper we introduce a new clustering method named GLC to process very large niixed incomplete data sets in order to obtain a partition in connected sets. © 2001 IEEE.
has publication venue: Proceedings - IEEE International Conference on Data Mining, ICDM
crisid: 8500
page end: 644
EI dscopus: 2-s2.0-78149329797

Information Content Entity
type: Class

Conference Paper
type: Class