Information-statistical data mining

warehouse integration with examples of Oracle basics
  • 289 Pages
  • 2.25 MB
  • 707 Downloads
  • English
by
Kluwer Academic , Boston
Oracle (Computer file), Data mining, Data wareho
Statementby Bon K. Sy, Arjun K. Gupta.
SeriesThe Kluwer international series in engineering and computer science -- SECS 757, The Kluwer international series in engineering and computer science -- SECS 757, Kluwer international series in engineering and computer science -- SECS 757.
ContributionsGupta, A. K. 1938-
Classifications
LC ClassificationsQA76.9.D343 S9 2004
The Physical Object
Paginationxxii, 289 p. :
ID Numbers
Open LibraryOL21486666M
ISBN 101402076509
LC Control Number2003064180
OCLC/WorldCa53288034

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Friedman, Springer-Verlag, ). The lectures will consist of high-quality projected presentations and discussion. Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation.

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Massive data sets pose a great challenge to many cross-disciplinary fields, including statistics. The high dimensionality and different data types and structures have now outstripped the capabilities of traditional statistical, graphical, and data visualization tools. Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics is designed for educated viewers composed of researchers and practitioners in business.

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Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics is designed for a professional audience composed of researchers and practitioners in industry.

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It is a book that covers many key. Statistics is a component of data mining that provides the tools and analytics techniques for dealing with large amounts of data. It is the science of learning from data and includes everything from collecting and organizing to analyzing and presenting data.

Statistics focuses on probabilistic models, specifically inference, using data. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

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Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and.

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I was an orthodox statistician until my struggles with the weaknesses of statistics within the big data setting of today were resolved. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine With it has come vast amounts of data in a variety of fields such /5.