More books in category: Data Analysis
by: Nikola K. Kasabov
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Covers the entire spectrum of topics from data collection and analysis to knowledge discovery, including statistical, machine learning, and soft computing methods
Provides the most complete and lucid discussion of knowledge discovery found in a single volume
Focuses on particular application domains from business to bioinformatics, engineering, and ecology
Contain numerous exercises, data sets, and software, making each chapter more meaningful
Offers further hands-on work available from the book's website, including all examples and solutions from the text
Considers numerous variables including the level of user expertise required
This book begins by addressing the fundamental differences between data, information, and knowledge. It then considers how data can be effectively characterized in light of its diverse forms and origins before looking at the various aspects of data acquisition and storage. The authors then deal with several topics related to information theory, including measurement, probability, and entropy, and consider how data is analyzed and transformed into information. Finally, they look to computational modeling for both problem solving and knowledge discovery, employing methods derived from statistics and computer science and discussing selection, use, and particular applications of these methods.
Table of Contents
Introduction to Data, Information, and Knowledge
Sources and Acquisition of Data
Information Theory
Visual Representation of Data
Modeling and Knowledge Discovery
Generic Problem-Solving Tasks and Techniques
Applications
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