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Symbolic Data Analysis: Conceptual Statistics and Data Mining

With the advent of computers, very large datasets have become routine. Standard statistical methods don't have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summ...

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Detalles Bibliográficos
Autores principales: Billard, Lynne, Diday, Edwin
Lenguaje:eng
Publicado: John Wiley & Sons 2012
Materias:
Acceso en línea:http://cds.cern.ch/record/1486774
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author Billard, Lynne
Diday, Edwin
author_facet Billard, Lynne
Diday, Edwin
author_sort Billard, Lynne
collection CERN
description With the advent of computers, very large datasets have become routine. Standard statistical methods don't have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible. One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal s
id cern-1486774
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2012
publisher John Wiley & Sons
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spelling cern-14867742021-04-22T00:16:00Zhttp://cds.cern.ch/record/1486774engBillard, LynneDiday, EdwinSymbolic Data Analysis: Conceptual Statistics and Data MiningMathematical Physics and Mathematics With the advent of computers, very large datasets have become routine. Standard statistical methods don't have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible. One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal sJohn Wiley & Sonsoai:cds.cern.ch:14867742012
spellingShingle Mathematical Physics and Mathematics
Billard, Lynne
Diday, Edwin
Symbolic Data Analysis: Conceptual Statistics and Data Mining
title Symbolic Data Analysis: Conceptual Statistics and Data Mining
title_full Symbolic Data Analysis: Conceptual Statistics and Data Mining
title_fullStr Symbolic Data Analysis: Conceptual Statistics and Data Mining
title_full_unstemmed Symbolic Data Analysis: Conceptual Statistics and Data Mining
title_short Symbolic Data Analysis: Conceptual Statistics and Data Mining
title_sort symbolic data analysis: conceptual statistics and data mining
topic Mathematical Physics and Mathematics
url http://cds.cern.ch/record/1486774
work_keys_str_mv AT billardlynne symbolicdataanalysisconceptualstatisticsanddatamining
AT didayedwin symbolicdataanalysisconceptualstatisticsanddatamining