This book provides a systematic overview and classification of tasks in data analysis, methods to solve them and typical problems encountered.Different views from classical and non-classical statistics like Bayesian inference and robust statistics, exploratory data analysis, data mining and machine learning are combined together to provide a better understanding of the methods, their potentials and limitations.Features:• Focuses on validation and pitfalls related to real world applications of these techniques• Presents different approaches, analysing their advantages and disadvantages for certain types of tasks including exploratory data analysis, data mining, classical statistics and robust statistics• Contains case studies and examples to enhance understanding• A supplementary website provides numerous hands-on examplesThis collective view of data analysis problems and methods, their potentials and limitations is an indispensable learning tool for graduate and advanced undergraduate students.

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 Hppner 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.