Data Mining: Concepts and Techniques - Rilegato

Han, Jiawei; Kamber, Micheline; Pei, Jian

Rilegato

ISBN 10: 0123814790 ISBN 13: 9780123814791

Casa editrice: Morgan Kaufmann Pub, 2011

Riassunto

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

Su questo libro

Informazioni sugli autori

Jiawei Han is Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has received many awards for his contributions in the field, including the 2004 ACM SIGKDD Innovations Award. He has served as Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data, and on editorial boards of several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.

Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master's degree in computer science (specializing in artificial intelligence) from Concordia University, Canada.

Jian Pei is currently a Canada Research Chair (Tier 1) in Big Data Science and a Professor in the School of Computing Science at Simon Fraser University. He is also an associate member of the Department of Statistics and Actuarial Science. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is recognized as a Fellow of the Association of Computing Machinery (ACM) for his “contributions to the foundation, methodology and applications of data mining” and as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for his “contributions to data mining and knowledge discovery”. He is the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE), a director of the Special Interest Group on Knowledge Discovery in Data (SIGKDD) of the Association for Computing Machinery (ACM), and a general co-chair or program committee co-chair of many premier conferences.

Dalla quarta di copertina

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.

Since the previous edition s publication, great advances have been made in the field of data mining. Not only does this Third Edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology; mining stream; mining social networks; and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today s most powerful data mining techniques.

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The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.

Since the previous edition s publication, great advances have been made in the field of data mining. Not only does this Third Edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology; mining stream; mining social networks; and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today s most powerful data mining techniques.

Dettagli dell?edizione

• Editore: Morgan Kaufmann Pub
• Data di pubblicazione: 2011
• Lingua: Inglese
• ISBN 10: 0123814790
• ISBN 13: 9780123814791
• Rilegatura: Copertina rigida
• Numero edizione: 3
• Numero di pagine: 703
• Contatto del produttore: non disponibile