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Handbook of Data Mining and Knowledge Discovery [Hardcover]

Willi Klï¿1/2sgen (Editor), the late Jan M. Zytkow (Editor), Willi Klosgen (Author), Jan Zyt (Author)
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Book Description

0195118316 978-0195118315 June 15, 2002 1
Data mining, or knowledge discovery in databases (KDD), is one of the fastest growing areas in computing application: it offers powerful tools to analyze the many large data bases used in business, science, and industry. Data mining technology searches large databases to extract information and patterns that can be translated into useful applications, such as classifying or predicting customer behavior. This book brings together fundamental knowledge on all aspects of data mining--concepts, theory, techniques, applications, and case studies. Designed for students and professionals in such fields as computing applications, information systems management and strategic research and management, the Handbook is a comprehensive guide to essential tools and technology, from neural networks to artificial intelligence. There is a strong emphasis on real-world case studies in such areas as banking, finance, marketing management, real estate, engineering, medicine, pharmacology, and the biosciences. A much needed resource on one of the fastest growing areas of computer applications--the development and use of tools to analyze, interpret, and make use of the enormous amounts of information stored in the world's databases.


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About the Author

Willi Klosgen, Principal Researcher, Fraunhofer Institute for Autonomous Intelligent Systems, Sankt Augustin.

Product Details

  • Hardcover: 1064 pages
  • Publisher: Oxford University Press, USA; 1 edition (June 15, 2002)
  • Language: English
  • ISBN-10: 0195118316
  • ISBN-13: 978-0195118315
  • Product Dimensions: 10.1 x 7.6 x 2.1 inches
  • Shipping Weight: 4.6 pounds
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #3,868,658 in Books (See Top 100 in Books)

 

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8 of 12 people found the following review helpful:
4.0 out of 5 stars Have Large Data sets to Analyse?, April 10, 2004
This review is from: Handbook of Data Mining and Knowledge Discovery (Hardcover)
As computer usage keeps increasing, the amount of data being stored on disks worldwide also rises. Many terabytes by now, if not a petabyte or two.

Companies often have gigabytes of data that need to be analysed. This book is a very useful compendium of techniques that can be applied to the data. Because it may be that a sophisticated understanding of your data is a strategic advantage to your company. If not a core competence.

The book covers data in many different fields. From financial, engineering, medical and even bioinformatic. The methods applied to these data span the gamut from Artificial Intelligence to neural networks. While some methods are specific to the type of data being studied, the astute reader may be able to modify these for her own situation.

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Inside This Book (learn more)
First Sentence:
This section introduces knowledge discovery in databases (KDD) as a field that is driven by the need for knowledge derived from massive and varied data. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
class distribution information, break detection systems, value reduct, cellular cloning fraud, feature subset generation, subgroup mining, term generation stage, equation finder, actual lead exposure, rough set framework, classical rough set approach, incoming data record, multicriteria classification, relative redact, knowledge discovery algorithms, lazy decision trees, heuristic pruning rules, information granules, administrational tasks, descriptive data mining, data mining goals, data warehouse administration, multistrategy discovery assistant, online analytical mining, rule set induced
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, New York, International Conference, Menlo Park, San Francisco, San Mateo, Enterprise Miner, Kluwer Academic Publishers, Document Explorer, Berlin Springer-Verlag, Knozoledge Discovery, United States, Oxford University Press, Los Alamitos, European Conference, Visual Insights, Data Engng, International Workshop, Cambridge University Press, Silicon Graphics, World Wide Web, Computer Society Press, Englewood Cliffs, John Wiley, Maclnne Learning
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This book cites 23 books:
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