Symbolic data analysis (SDA) plays a key role in the interaction between statistics and data processing. Through an extension of the concepts employed in data mining, this work provides an advanced guide to the techniques required to analyze symbolic data. It also provides an introduction to the SODAS software, which is complementary to existing data analysis software that is unable to work with symbolic data. After an introductory mathematical framework, sections cover databases versus symbolic objects, unsupervised and supervised methods, and applications of the SODAS software. A supporting web site hosts the software. The book is aimed primarily at practitioners of symbolic data analysis, such as statisticians and economists, in the public and private sectors. It will also be of interest to postgraduate students and researchers in web mining, text mining, and bioengineering. Diday is affiliated with the University of Paris IX, France. Noirhomme-Fraiture is affiliated with the University of Namur, Belgium. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)
Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such data. Symbolic data methods differ from that of data mining, for example, because rather than identifying points of interest in the data, symbolic data methods allow the user to build models of the data and make predictions about future events.
This book is the result of the work f a pan-European project team led by Edwin Diday following 3 years work sponsored by EUROSTAT. It includes a full explanation of the new SODAS software developed as a result of this project. The software and methods described highlight the crossover between statistics and computer science, with a particular emphasis on data mining.
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