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Data Mining
Concepts and Techniques
Résumé
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.
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.
L'auteur - Jiawei Han
Jiawei Han is Director of the Intelligent Database Systems Research Laboratory and Professor in the School of Computing Science at Simon Fraser University. Well known for his research in the areas of data mining and database systems, he has served on program committees for dozens of international conferences and workshops and on editorial boards for several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.
L'auteur - Micheline Kamber
Micheline Kamber is a researcher and freelance technical writer with an M.S. in Computer Science (Artificial Intelligence). She is a member of the Intelligent Database Systems Research Laboratory at Simon Fraser University.
Sommaire
- Introduction
- Data preprocessing
- Data warehouse and OLAP technology : an overview
- Data cube computation and data generalization
- Mining frequent patterns, associations, and correlations
- Classification and prediction
- Cluster analysis
- Mining stream, time-series, and sequence data
- Graph mining, social network analysis, and multirelational data mining
- Mining object, spatial, multimedia, text, and Web data
- Applications and trends in data mining
- Appendix : An introduction to Microsoft's OLE DB for data mining
Caractéristiques techniques
PAPIER | |
Éditeur(s) | Morgan Kaufmann |
Auteur(s) | Jiawei Han, Micheline Kamber |
Parution | 15/04/2006 |
Édition | 2eme édition |
Nb. de pages | 800 |
Format | 19,5 x 24 |
Couverture | Relié |
Poids | 1735g |
Intérieur | Noir et Blanc |
EAN13 | 9781558609013 |
ISBN13 | 978-1-55860-901-3 |
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