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Generically Dependent Continuant
Proceedings - IEEE International Conference on Data Mining, ICDM
RDF description of A clustering method for very large mixed data sets - http://orbis.uaslp.mx/vivo/individual/scopus8499
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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.
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.
A clustering method for very large mixed data sets
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