Overview
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.
Full Product Details
Author: Michael R. Berthold ,
Christian Borgelt ,
Frank Höppner ,
Frank Klawonn
Publisher: Springer London Ltd
Imprint: Springer London Ltd
Volume: v. 42
Dimensions:
Width: 15.50cm
, Height: 2.80cm
, Length: 23.50cm
Weight: 0.850kg
ISBN: 9781848822597
ISBN 10: 1848822596
Pages: 394
Publication Date: 01 July 2010
Audience:
Professional and scholarly
,
Professional & Vocational
Replaced By: 9783030455736
Format: Hardback
Publisher's Status: Out of Print
Availability: In Print
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.
Reviews
From the reviews: The clear and complete exposition of arguments, along with the attention to formalization and the balanced number of bibliographic references, make this book a bright introduction to intelligent data analysis. It is an excellent choice for graduate or advanced undergraduate courses, as well as for researchers and professionals who want get acquainted with this field of study. ! Overall, the authors hit their target producing a textbook that aids in understanding the basic processes, methods, and issues for intelligent data analysis. (Corrado Mencar, ACM Computing Reviews, April, 2011)
From the reviews: The authors, leading scholars in the field based in Germany and Spain, seek to offer a hands-on instructional approach to basic data analysis techniques and consider their use in solving problems. The reader is taken through the process, following the interlinked steps of project understanding, data understanding, data preparation, modelling, and deployment and monitoring. The text reviews the basics of classical statistics that support and justify many data analysis methods, and includes a glossary of statistical terms. (Times Higher Education, 26 May 2011) The clear and complete exposition of arguments, along with the attention to formalization and the balanced number of bibliographic references, make this book a bright introduction to intelligent data analysis. It is an excellent choice for graduate or advanced undergraduate courses, as well as for researchers and professionals who want get acquainted with this field of study. ! Overall, the authors hit their target producing a textbook that aids in understanding the basic processes, methods, and issues for intelligent data analysis. (Corrado Mencar, ACM Computing Reviews, April, 2011)
