Data Mining

Concepts and Techniques

Jiawei Han, Micheline Kamber

800 pages, parution le 15/04/2006 (2^eme édition)

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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

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Morgan Kaufmann
Auteur(s)	Jiawei Han, Micheline Kamber
Parution	15/04/2006
Édition	2^eme 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