Foundations Of Intelligent Data Analysis
By: Berthold, Michael R. (University Of Konstanz) (Author), Borgelt, Christian (Author), Hoppner, Frank (Author), Klawonn, Frank (University Of Ostriesland, Emden, Germany) (Author).
Springer London Ltd. Published: 00/08/2010. Audience Guide: Professional & Vocational.
Mixed media product. Sourced from U.S.A.
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 examples This collective view of data analysis problems and methods, their potentials and limitations is an indispensable learning tool for graduate and advanced undergraduate students. Item Details
ISBN10/13: 1848822596/9781848822597
TITLE: Foundations of Intelligent Data Analysis
CONTRIBUTORS: Berthold, Michael R. (University Of Konstanz) (Author), Borgelt, Christian (Author), Hoppner, Frank (Author), Klawonn, Frank (University Of Ostriesland, Emden, Germany) (Author)
IMPRINT: Springer London Ltd
PUBLISHER: Springer London Ltd
FORMAT: Mixed media product
PUBLICATION DATE: 00/08/2010
SUBJECT: Computers/Internet, Computers/Internet, Applications Of Computing, General Theory Of Computing
DIMENSIONS (Width x Height): 155mm x 235mm
PAGES: 450
AUDIENCE GUIDE: Professional & Vocational
CONTENTS: Introduction.- Practical Data Analysis: An Overview.- What is Intelligent Data Analysis?.- Business Understanding.- Data Understanding.- Data Preparation.- Objectives, Algorithms and Validation.- Finding Structures in Data.- Finding Explanations.- Finding Predictors.- Advanced Topics.- Appendix: Statistical Background
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