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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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Lenguaje: | eng |
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John Wiley & Sons
2012
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Acceso en línea: | http://cds.cern.ch/record/1486774 |
_version_ | 1780926172515794944 |
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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 |
record_format | invenio |
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 |