Advanced Data Mining Techniques
Autor David L. Olson, Dursun Delenen Limba Engleză Paperback – 21 ian 2008
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Specificații
ISBN-13: 9783540769163
ISBN-10: 3540769161
Pagini: 192
Ilustrații: XII, 180 p. 21 illus.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.3 kg
Ediția:2008
Editura: Springer Berlin, Heidelberg
Colecția Springer
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540769161
Pagini: 192
Ilustrații: XII, 180 p. 21 illus.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.3 kg
Ediția:2008
Editura: Springer Berlin, Heidelberg
Colecția Springer
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Data Mining Process.- Data Mining Methods As Tools.- Memory-Based Reasoning Methods.- Association Rules in Knowledge Discovery.- Fuzzy Sets in Data Mining.- Rough Sets.- Support Vector Machines.- Genetic Algorithm Support to Data Mining.- Performance Evaluation for Predictive Modeling.- Applications.- Applications of Methods.
Recenzii
From the reviews:
"Text analysis and data mining have become increasingly important capabilities in today’s information-flooded world, and choosing the right technique makes all the difference. This book contains some advanced data mining techniques, but also includes an overview of important data mining fundamentals, specifically the CRISP-DM and SEMMA industry standards. … Summing Up: Recommended. Upper-division undergraduates and up." (H. J. Bender, CHOICE, Vol. 45 (11), August, 2008)
"Text analysis and data mining have become increasingly important capabilities in today’s information-flooded world, and choosing the right technique makes all the difference. This book contains some advanced data mining techniques, but also includes an overview of important data mining fundamentals, specifically the CRISP-DM and SEMMA industry standards. … Summing Up: Recommended. Upper-division undergraduates and up." (H. J. Bender, CHOICE, Vol. 45 (11), August, 2008)
Textul de pe ultima copertă
This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining. Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.
Caracteristici
Includes supplementary material: sn.pub/extras