Data Mining: Know It All
Authors: Chakrabarti Soumen, Cox Earl, Frank Eibe, Güting Ralf Hartmut, Han Jiawei, Jiang Xia, Kamber Micheline, Lightstone Sam S., Nadeau Thomas P., Neapolitan Richard E., Pyle Dorian, Refaat Mamdouh, Schneider Markus, Teorey Toby J., Witten Ian H.Language: Anglais
In Print (Delivery period: 14 days).Add to cart the livre of Chakrabarti Soumen, Cox Earl, Frank Eibe, Güting Ralf Hartmut, Han Jiawei, Jiang Xia, Kamber Micheline, Lightstone Sam S., Nadeau Thomas P., Neapolitan Richard E., Pyle Dorian, Refaat Mamdouh, Schneider Markus, Teorey Toby J., Witten Ian H.
496 p. · 19.1x23.5 cm · Hardback
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.
Chapter 2: Data Acquisition and Integration
Chapter 3: Data Pre-processing
Chapter 4: Physical Design for Decision Support, Warehousing, and OLAP
Chapter 5: Algorithms - The Basic Methods
Chapter 6: Further Techniques in Decision Analysis
Chapter 7: Fundamental Concepts of Genetic Algorithms
Chapter 8: Spatio-Temporal Data Structures and Algorithms for Moving Objects Types
Chapter 9: Improving the Mined Model
Chapter 10: Web Mining - Social Network Analysis
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