Data Mining, Southeast Asia Edition: The Morgan Kaufmann Series in Data Management Systems
Autor Jiawei Han, Jian Pei, Micheline Kamberen Limba Engleză Paperback – 10 iun 2006
Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data.
- A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data
- Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning
- Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects
- Complete classroom support for instructors at www.mkp.com/datamining2e companion site
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Specificații
ISBN-13: 9780123739056
ISBN-10: 0123739055
Pagini: 800
Dimensiuni: 191 x 235 mm
Greutate: 1.35 kg
Ediția:2
Editura: ELSEVIER SCIENCE
Seria The Morgan Kaufmann Series in Data Management Systems
ISBN-10: 0123739055
Pagini: 800
Dimensiuni: 191 x 235 mm
Greutate: 1.35 kg
Ediția:2
Editura: ELSEVIER SCIENCE
Seria The Morgan Kaufmann Series in Data Management Systems
Public țintă
database R&D professionals, data mining professionals; undergraduate and graduate students who will want to incorporate data mining as part of their knowledge base and expertise.Cuprins
1. Introduction
2. Data Preprocessing
3. Data Warehouse and OLAP Technology: An Overview
4. Data Cube Computation and Data Generalization
5. Mining Frequent Patterns, Associations, and Correlations
6. Classification and Prediction
7. Cluster Analysis
8. Mining Stream, Time-Series, and Sequence Data
9 Graph Mining, Social Network Analysis, and Multi-Relational Data Mining
10. Mining Object, Spatial, Multimedia, Text, and Web Data
11. Applications and Trends in Data Mining
Appendix A: An Introduction to Microsoft's OLE DB for Data Mining
2. Data Preprocessing
3. Data Warehouse and OLAP Technology: An Overview
4. Data Cube Computation and Data Generalization
5. Mining Frequent Patterns, Associations, and Correlations
6. Classification and Prediction
7. Cluster Analysis
8. Mining Stream, Time-Series, and Sequence Data
9 Graph Mining, Social Network Analysis, and Multi-Relational Data Mining
10. Mining Object, Spatial, Multimedia, Text, and Web Data
11. Applications and Trends in Data Mining
Appendix A: An Introduction to Microsoft's OLE DB for Data Mining