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Data Mining: Know It All
 
 
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Data Mining: Know It All [Hardcover]

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

0123746299 978-0123746290 November 26, 2008 1
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.

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Editorial Reviews

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.

About the Author

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.

Earl founded and serves as President of, Scianta Intelligence, a next generation machine intelligence and knowledge exploration company. He is a futurist, author, management consultant, and educator involved in discovering the epistemology of advanced intelligent systems, the redefinition of the machine mind, and, as a pioneer of Internet-based technologies, the way in which evolving inter-connected virtual worlds will affect the sociology of business and culture in the near and far future. 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.

Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten, and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now an associate professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.>

Jiawei Han is Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has received many awards for his contributions in the field, including the 2004 ACM SIGKDD Innovations Award. He has served as Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data, and on editorial boards of several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.

Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master's degree in computer science (specializing in artificial intelligence) from Concordia University, Canada.

Sam Lightstone is a Senior Technical Staff Member and Development Manager with IBM's DB2 product development team. His work includes numerous topics in autonomic computing and relational database management systems. He is cofounder and leader of DB2's autonomic computing R&D effort. He is Chair of the IEEE Data Engineering Workgroup on Self Managing Database Systems and a member of the IEEE Computer Society Task Force on Autonomous and Autonomic Computing. In 2003 he was elected to the Canadian Technical Excellence Council, the Canadian affiliate of the IBM Academy of Technology. He is an IBM Master Inventor with over 25 patents and patents pending; he has published widely on autonomic computing for relational database systems. He has been with IBM since 1991.

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 PowerhouseT 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.

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.

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.


Product Details

  • Hardcover: 480 pages
  • Publisher: Morgan Kaufmann; 1 edition (November 26, 2008)
  • Language: English
  • ISBN-10: 0123746299
  • ISBN-13: 978-0123746290
  • Product Dimensions: 9.5 x 7.5 x 1.4 inches
  • Shipping Weight: 2.5 pounds (View shipping rates and policies)
  • Average Customer Review: 1.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #1,551,144 in Books (See Top 100 in Books)

More About the Author

Sam Lightstone has briefed dozens of leading companies and universities on technology careers, trends, and emerging areas of software research in his 20 years of employment at IBM. A sought after public speaker, author, and prolific inventor, he currently works on product strategy and product architecture for one of the world's largest commercial software products and spends a good part of his professional time recruiting and mentoring software engineers.

You can learn more about Sam at his personal home page lightstone.x10hosting.com, or at his consulting company page www.MakingItBigCareers.com


 

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3 of 14 people found the following review helpful:
1.0 out of 5 stars Great Idea Spoiled by Slipshod Editing, November 19, 2008
This review is from: Data Mining: Know It All (Hardcover)
Part of the value of a book like this is to get relevant, well-focused pointers into the larger literature. Some chapters have "resources" sections; some "resources" sections have bibliographies. There is no consistent pattern to the coverage. Finally, and most damaging, many resources are simply missing: "Hartigan, 1975" is all you'll get to find out more about variations of k-means clustering.

I hope the publisher will rectify this by providing adequate bibliographical materials on their website or in later editions.
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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
data preprocessing, social network analysis, reformatting data, temporal data types, false yes rainy, constructing rules, crew assignments, covering algorithms, mining association rules, data rollup, smoothed residual variance estimate, input battery variables, anachronistic variables, residual test model, many mining tools, concept hierarchy generation, data warehouse bus, contact lens data, numerosity reduction, attribute subset selection, predictions unsatisfactory, clique attack, multiresponse linear regression, solution terrain, data discretization
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Solving Problems, Improving Model Quality, Improving the Model, Naïve Bayes, The Basic Methods, Further Techniques, Decision Analysis, Algorithm Adjustment, Simple Examples, Descriptive Data Summarization, Morgan Kaufmann, Child Child, Constructing Decision Trees, Balance Status, Delta Baker Charlie Echo Able, John Wiley, Job Cost Genomes, Statistical Modeling, Modeling Risk Preferences, Complex Systems, World Wide Web Conference, Age Status, Suppose Joe, Absent Leaf, Netezza Corporation
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Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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