
Data Mining: Know It All
Description
Key Features
- Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints.
- Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader’s technical expertise and ability to implement practical solutions.
- Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.
Readership
Table of Contents
- Chapter 1: Data Mining Overview
Chapter 2: Data Acquisition and Integration
Chapter 3: Data Pre-processing
Chapter 4: Physical Design for Decision Support, Warehousing, and OLAP
Chapter 5: Algorithms - The Basic Methods
Chapter 6: Further Techniques in Decision Analysis
Chapter 7: Fundamental Concepts of Genetic Algorithms
Chapter 8: Spatio-Temporal Data Structures and Algorithms for Moving Objects Types
Chapter 9: Improving the Mined Model
Chapter 10: Web Mining - Social Network Analysis
Product details
- No. of pages: 480
- Language: English
- Copyright: © Morgan Kaufmann 2008
- Published: October 31, 2008
- Imprint: Morgan Kaufmann
- eBook ISBN: 9780080877884
- Hardcover ISBN: 9780123746290
About the Authors
Soumen Chakrabarti
Affiliations and Expertise
Earl Cox
Earl has over thirty years experience in managing and participating in the software development process at the system as well as tightly integrated application level. In the area of advanced machine intelligence technologies, Earl is a recognized expert in fuzzy logic, and adaptive fuzzy systems as they are applied to information and decision theory. He has pioneered the integration of fuzzy neural systems with genetic algorithms and case-based reasoning. As an industry observer and futurist, Earl has written and talked extensively on the philosophy of the Response to Change, the nature of Emergent Intelligence, and the Meaning of Information Entropy in Mind and Machine.
Affiliations and Expertise
Eibe Frank
Affiliations and Expertise
Ralf Güting
Jiawei Han
Affiliations and Expertise
Xia Jiang
Affiliations and Expertise
Micheline Kamber
Affiliations and Expertise
Sam Lightstone
Affiliations and Expertise
Thomas Nadeau
Richard E. Neapolitan
Affiliations and Expertise
Dorian Pyle
Dorian Pyle is Chief Scientist and Founder of PTI (www.pti.com), which develops and markets Powerhouse™ predictive and explanatory analytics software. Dorian has over 20 years experience in artificial intelligence and machine learning techniques which are used in what is known today as “data mining” or “predictive analytics”. He has applied this knowledge as a consultant with Knowledge Stream Partners, Xchange, Naviant, Thinking Machines, and Data Miners and with various companies directly involved in credit card marketing for banks and with manufacturing companies using industrial automation. In 1976 he was involved in building artificially intelligent machine learning systems utilizing the pioneering technologies that are currently known as neural computing and associative memories. He is current in and familiar with using the most advanced technologies in data mining including: entropic analysis (information theory), chaotic and fractal decomposition, neural technologies, evolution and genetic optimization, algebra evolvers, case-based reasoning, concept induction and other advanced statistical techniques.
Affiliations and Expertise
Mamdouh Refaat
During his career, Mamdouh has managed numerous data mining consulting projects in marketing, CRM, and credit risk for Fortune 500 organizations in North America and Europe. In addition, he has delivered over 50 professional training courses in data mining and business analytics.
Mamdouh holds a PhD in Engineering from the University of Toronto, and an MBA from the University of Leeds.
Affiliations and Expertise
Markus Schneider
Affiliations and Expertise
Toby Teorey
Toby J. Teorey is a professor in the Electrical Engineering and Computer Science Department at the University of Michigan, Ann Arbor. He received his B.S. and M.S. degrees in electrical engineering from the University of Arizona, Tucson, and a Ph.D. in computer sciences from the University of Wisconsin, Madison. He was general chair of the 1981 ACM SIGMOD Conference and program chair for the 1991 Entity-Relationship Conference. Professor Teorey’s current research focuses on database design and data warehousing, OLAP, advanced database systems, and performance of computer networks. He is a member of the ACM and the IEEE Computer Society.
Affiliations and Expertise
Ian Witten
Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.