Description
Data Mining: Know It All
Authors: 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.
Language: EnglishSubjects for Data Mining: Know It All:
72.84 €
Subject to availability at the publisher.
Add to cart the book of 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.480 p. · 19x23.3 cm · Hardback
Description
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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 k
- 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.