Data Mining: Know It All - Hardcover

Chakrabarti, Soumen; Neapolitan, Richard E.; Pyle, Dorian; Refaat, Mamdouh; Schneider, Markus; Teorey, Toby J.; Witten, Ian H.; Cox, Earl; Frank, Eibe; Güting, Ralf Hartmut; Han, Jiawei; Jiang, Xia; Kamber, Micheline; Lightstone, Sam S.; Nadeau, Thomas P.

Hardcover

ISBN 10: 0123746299 ISBN 13: 9780123746290

Publisher: Morgan Kaufmann, 2009

Synopsis

This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.

• 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.

About this title

About the Authors

Soumen Chakrabarti is assistant Professor in Computer Science and Engineering at the Indian Institute of Technology, Bombay. Prior to joining IIT, he worked on hypertext databases and data mining at IBM Almaden Research Center. He has developed three systems and holds five patents in this area. Chakrabarti has served as a vice-chair and program committee member for many conferences, including WWW, SIGIR, ICDE, and KDD, and as a guest editor of the IEEE TKDE special issue on mining and searching the Web. His work on focused crawling received the Best Paper award at the 8th International World Wide Web Conference (1999). He holds a Ph.D. from the University of California, Berkeley.

Richard E. Neapolitan is professor and Chair of Computer Science at Northeastern Illinois University. He has previously written four books including the seminal 1990 Bayesian network text Probabilistic Reasoning in Expert Systems. More recently, he wrote the 2004 text Learning Bayesian Networks, the textbook Foundations of Algorithms, which has been translated to three languages and is one of the most widely-used algorithms texts world-wide, and the 2007 text Probabilistic Methods for Financial and Marketing Informatics (Morgan Kaufmann Publishers).

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.

Mamdouh Refaat is a data mining and business analytics consultant advising major organizations in North America and Europe. He has held several positions in consulting organizations and software vendors, including the director of consulting services at ANGOSS Software Corporation, a global data mining software and service provider. 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 Ph.D. in Engineering from the University of Toronto, and an MBA from the University of Leeds.

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.

Markus Schneider is an Assistant Professor in the Computer Science Department of the University of Florida and holds a doctoral degree in Computer Science from the University of Hagen, Germany. He is author of a monograph in the area of spatial databases and of a German textbook on implementation concepts for database systems, and has published about 40 articles on database systems. He is on the editorial board of GeoInformatica.

From the Back Cover

All of the elements of data mining together in a single volume written by the best and brightest experts in the field!

Enterprise data volume is growing at an exponential rate, and the intelligent analysis of this data is now a top-rated priority for every organization. As data sets grow in size and complexity, there is a shift toward automatic data analysis using ever more complex and sophisticated tools and processes.

Until now, information about these mining methodologies has been scattered across many different books as there has been no one-stop reference on how to successfully implement them. Data Mining: Know It All is here, assembled from the works of leading researchers and practitioners. This best-of-the-best collection delivers a wide-ranging, detailed examination of data mining that no book by a single author can possibly match. From data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology, Data Mining: Know It All is the complete insider’s guide to the essentials of data mining and analysis.

• Features chapters contributed by recognized experts in the field, letting you remain up-to-date and fully informed from multiple viewpoints.
• Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing your technical expertise and ability to implement practical solutions.
• Covers both theory and practice, bringing all of the elements of data mining together in a single volume and saving you the time and expense of making multiple purchases.

Edition details

• Publisher: Morgan Kaufmann
• Publication date: 2009
• Language: English
• ISBN 10: 0123746299
• ISBN 13: 9780123746290
• Binding: Hardcover
• Number of pages: 480