Each passing year bears witness to the development of ever more powerful
computers, increasingly fast and cheap storage media, and even higher
bandwidth data connections. This makes it easy to believe that we can
now - at least in principle - solve any problem we are faced with so
long as we only have enough data. Yet this is not the case. Although
large databases allow us to retrieve many different single pieces of
information and to compute simple aggregations, general patterns and
regularities often go undetected. Furthermore, it is exactly these
patterns, regularities and trends that are often most valuable. To avoid
the danger of "drowning in information, but starving for knowledge" the
branch of research known as data analysis has emerged, and a
considerable number of methods and software tools have been developed.
However, it is not these tools alone but the intelligent application of
human intuition in combination with computational power, of sound
background knowledge with computer-aided modeling, and of critical
reflection with convenient automatic model construction, that results in
successful intelligent data analysis projects. Guide to Intelligent Data
Analysis provides a hands-on instructional approach to many basic data
analysis techniques, and explains how these are used to solve data
analysis problems. Topics and features: guides the reader through the
process of data analysis, following the interdependent steps of project
understanding, data understanding, data preparation, modeling, and
deployment and monitoring; equips the reader with the necessary
information in order to obtain hands-on experience of the topics under
discussion; provides a review of the basics of classical statistics that
support and justify many data analysis methods, and a glossary of
statistical terms; includes numerous examples using R and KNIME,
together with appendices introducing the open source software;
integrates illustrations and case-study-style examples to support
pedagogical exposition. This practical and systematic textbook/reference
for graduate and advanced undergraduate students is also essential
reading for all professionals who face data analysis problems. Moreover,
it is a book to be used following one's exploration of it. Dr. Michael
R. Berthold is Nycomed-Professor of Bioinformatics and Information
Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is
Principal Researcher at the Intelligent Data Analysis and Graphical
Models Research Unit of the European Centre for Soft Computing, Spain.
Dr. Frank Höppner is Professor of Information Systems at Ostfalia
University of Applied Sciences, Germany. Dr. Frank Klawonn is a
Professor in the Department of Computer Science and Head of the Data
Analysis and Pattern Recognition Laboratory at Ostfalia University of
Applied Sciences, Germany. He is also Head of the Bioinformatics and
Statistics group at the Helmholtz Centre for Infection Research,
Braunschweig, Germany.
