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Concepts et techniques d'exploration de données série Morgan Kaufmann en gestion de données
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Numéro de l'objet eBay :257028637775
Dernière mise à jour le 23 juil. 2025 03:38:29 CEST. Afficher toutes les modificationsAfficher toutes les modifications
Caractéristiques de l'objet
- État
- Book Title
- Data Mining: Concepts and Techniques (The Morgan Kaufmann Series,
- Narrative Type
- Management Information Systems
- Genre
- N/A
- Topic
- Management Information Systems
- Intended Audience
- N/A
- ISBN
- 9780123814791
À propos de ce produit
Product Identifiers
Publisher
Elsevier Science & Technology
ISBN-10
0123814790
ISBN-13
9780123814791
eBay Product ID (ePID)
102892165
Product Key Features
Number of Pages
744 Pages
Publication Name
Data Mining: concepts and Techniques
Language
English
Publication Year
2011
Subject
Intelligence (Ai) & Semantics, Databases / Data Mining, Databases / General
Type
Textbook
Subject Area
Computers
Series
The Morgan Kaufmann Series in Data Management Systems Ser.
Format
Hardcover
Dimensions
Item Length
9.2 in
Item Width
7.5 in
Additional Product Features
Edition Number
3
Intended Audience
College Audience
LCCN
2011-010635
Reviews
We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses.-- Gregory Piatetsky, President, KDnuggets Jiawei, Micheline, and Jian give an encyclopaedic coverage of all the related methods, from the classic topics of clustering and classification, to database methods (association rules, data cubes) to more recent and advanced topics (SVD/PCA , wavelets, support vector machines).. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book.- From the foreword by Christos Faloutsos, Carnegie Mellon University, We are living in the data deluge age. The "Data Mining: Concepts and Techniques" shows us how to find useful knowledge in all that data. The 3rd edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The book also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types and important application areas. The book, with its companion website, would make a great textbook for Analytics, Data Mining, and Knowledge Discovery courses-- Gregory Piatetsky, President, KDnuggets Jiawei, Micheline, and Jian give an encyclopedic coverage of all the related methods, from the classic topics of clustering and classification, to database methods (association rules, data cubes) to more recent and advanced topics (SVD/PCA , wavelets, support vector machines).. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book.- From the foreword by Christos Faloutsos, Carnegie Mellon University, "This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers."-- ACM's Computing Reviews.com We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses.-- Gregory Piatetsky, President, KDnuggets Jiawei, Micheline, and Jian give an encyclopaedic coverage of all the related methods, from the classic topics of clustering and classification, to database methods (association rules, data cubes) to more recent and advanced topics (SVD/PCA , wavelets, support vector machines).. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book.- From the foreword by Christos Faloutsos, Carnegie Mellon University "A very good textbook on data mining, this third edition reflects the changes that are occurring in the data mining field. It adds cited material from about 2006, a new section on visualization, and pattern mining with the more recent cluster methods. It's a well-written text, with all of the supporting materials an instructor is likely to want, including Web material support, extensive problem sets, and solution manuals. Though it serves as a data mining text, readers with little experience in the area will find it readable and enlightening. That being said, readers are expected to have some coding experience, as well as database design and statistics analysis knowledge.Two additional items are worthy of note: the text's bibliography is an excellent reference list for mining research; and the index is very complete, which makes it easy to locate information. Also, researchers and analysts from other disciplines--for example, epidemiologists, financial analysts, and psychometric researchers--may find the material very useful."-- Computing Reviews "Han (engineering, U. of Illinois-Urbana-Champaign), Micheline Kamber, and Jian Pei (both computer science, Simon Fraser U., British Columbia) present a textbook for an advanced undergraduate or beginning graduate course introducing data mining. Students should have some background in statistics, database systems, and machine learning and some experience programming. Among the topics are getting to know the data, data warehousing and online analytical processing, data cube technology, cluster analysis, detecting outliers, and trends and research frontiers. Chapter-end exercises are included." --SciTech Book News "This book is an extensive and detailed guide to the principal ideas, techniques and technologies of data mining. The book is organised in 13 substantial chapters, each of which is essentially standalone, but with useful references to the book's coverage of underlying concepts. A broad range of topics are covered, from an initial overview of the field of data mining and its fundamental concepts, to data preparation, data warehousing, OLAP, pattern discovery and data classification. The final chapter describes the current state of data mining research and active research areas." --BCS.org
Dewey Edition
22
Illustrated
Yes
Dewey Decimal
006.3/12
Table Of Content
1. Introduction2. Getting to Know Your Data3. Preprocessing: Data Reduction, Transformation, and Integration4. Data Warehousing and On-Line Analytical Processing5. Data Cube Technology 6. Mining Frequent Patterns, Associations and Correlations: Concepts and Methods7. Advanced Frequent Pattern Mining8. Classification: Basic Concepts9. Classification: Advanced Methods10. Cluster Analysis: Basic Concepts and Methods11. Cluster Analysis: Advanced Methods12. Outlier Analysis13. Trends and Research Frontiers in Data Mining
Synopsis
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining., Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
LC Classification Number
QA76.9.D343
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ValueVort3x
100% d'évaluations positives•396 objets vendus
Inscrit comme vendeur particulierEn conséquence, les droits des consommateurs découlant de la législation européenne ne s'appliquent pas. La Garantie client eBay continue de s'appliquer pour la plupart des achats.
Évaluations du vendeur (126)
- a***e (275)- Évaluations laissées par l'acheteur.Dernier moisAchat vérifiéOK
- b***a (42)- Évaluations laissées par l'acheteur.Dernier moisAchat vérifiéLooks good thanks
- 1***l (95)- Évaluations laissées par l'acheteur.Dernier moisAchat vérifiéShipped timely, very well packaged. In great condition as shown. Thank you!