Symbolic Data Analysis and the SODAS Software - Hardcover

Über die Autorin bzw. den Autor

Edwin Diday, Centre De Recherche en Mathématiques de la Décision, Université Paris 9, France
Edwin is a Professor of Computer Science, with 50 published papers, and 14 authored or edited books to his name. He has led international research teams in Symbolic Data Analysis, and is the founder of the field.

M. Noirhomme-Fraiture, Institute of Computer Science, University of Namur, Belgium
Monique Noirhomme-Fraiture is Professor and Head of the Unit of Applied Mathematics at the above faculty. She is involved in several HCI projects as well as having organized conferences and workshops within this field. She has contributed to 28 published papers and co-authored 2 books.

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Classical statistical techniques are often inadequate when it comes to analysing some of the large and internally variable datasets common today. Symbolic Data Analysis (SDA) has evolved in response to this problem and is a vital tool for summarizing information in such a way that the resulting data is of a manageable size. Symbolic data, represented by

intervals, lists, histograms, distributions, curves and the like, keeps the "internal variation" of summaries better than standard data. SDA therefore plays a key role in the interaction between statistics and data processing, and has established itself as an important tool for analysing official statistics.

Through an extension of the concepts employed in data mining, the Editors provide an advanced guide to the techniques required to analyse symbolic data. Contributions from leading experts in the field enable the reader to build models and make predictions about future events.

The book:

• Provides new graphical tools for the interpretation of large data sets.

• Extends standard statistics, data analysis, data mining and knowledge discovery to symbolic data.

• Introduces the SODAS software, which is complementary to existing data analysis software (e.g. SAS, SPSS, SPAD) that are unable to work on symbolic data.

• Induces, exports, and compares knowledge from one database to another.

• Features a supporting website hosting the software, and user manual.

Symbolic Data Analysis and the SODAS Software is primarily aimed at practitioners of symbolic data analysis, such as statisticians and economists, within both the public and private sectors. There is also much of interest to postgraduate students and researchers within web mining, text mining, and bioengineering.

Aus dem Klappentext

Classical statistical techniques are often inadequate when it comes to analysing some of the large and internally variable datasets common today. Symbolic Data Analysis (SDA) has evolved in response to this problem and is a vital tool for summarizing information in such a way that the resulting data is of a manageable size. Symbolic data, represented by

intervals, lists, histograms, distributions, curves and the like, keeps the "internal variation" of summaries better than standard data. SDA therefore plays a key role in the interaction between statistics and data processing, and has established itself as an important tool for analysing official statistics.

Through an extension of the concepts employed in data mining, the Editors provide an advanced guide to the techniques required to analyse symbolic data. Contributions from leading experts in the field enable the reader to build models and make predictions about future events.

The book:

• Provides new graphical tools for the interpretation of large data sets.

• Extends standard statistics, data analysis, data mining and knowledge discovery to symbolic data.

• Introduces the SODAS software, which is complementary to existing data analysis software (e.g. SAS, SPSS, SPAD) that are unable to work on symbolic data.

• Induces, exports, and compares knowledge from one database to another.

• Features a supporting website hosting the software, and user manual.

Symbolic Data Analysis and the SODAS Software is primarily aimed at practitioners of symbolic data analysis, such as statisticians and economists, within both the public and private sectors. There is also much of interest to postgraduate students and researchers within web mining, text mining, and bioengineering.