Symbolic Data Analysis: Conceptual Statistics and Data Mining

With the advent of computers, very large datasets have becomeroutine. Standard statistical methods don't have the power orflexibility to analyse these efficiently, and extract the requiredknowledge. An alternative approach is to summarize a large datasetin such a way that the resulting summary dataset is of a manageablesize and yet retains as much of the knowledge in the originaldataset as possible. One consequence of this is that the data mayno longer be formatted as single values, but be represented bylists, intervals, distributions, etc. The summarized data havetheir own internal structure, which must be taken into account inany analysis.

This text presents a unified account of symbolic data, how theyarise, and how they are structured. The reader is introduced tosymbolic analytic methods described in the consistent statisticalframework required to carry out such a summary and subsequentanalysis.

• Presents a detailed overview of the methods and applications ofsymbolic data analysis.
• Includes numerous real examples, taken from a variety ofapplication areas, ranging from health and social sciences, toeconomics and computing.
• Features exercises at the end of each chapter, enabling thereader to develop their understanding of the theory.
• Provides a supplementary website featuring links to downloadthe SODAS software developed exclusively for symbolic dataanalysis, data sets, and further material.

Primarily aimed at statisticians and data analysts, SymbolicData Analysis is also ideal for scientists working on problemsinvolving large volumes of data from a range of disciplines, including computer science, health and the social sciences. Thereis also much of use to graduate students of statistical dataanalysis courses.