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Data mining: concepts and techniques

By: Han, Jiawei.
Contributor(s): Kamber, Micheline | Pei, Jian.
Material type: materialTypeLabelBookSeries: The Morgan Kaufmann Series in Data Management Systems. Publisher: Waltham Morgan Kaufmann 2012Edition: 3rd.Description: xxxv, 703 p.ISBN: 9789380931913.Subject(s): Data mining | Computer science | Computers - Database management - Data miningDDC classification: 005.741 Summary: 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. Key Features • 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 Readership Data warehouse engineers, data mining professionals, database researchers, statisticians, data analysts, data modelers, and other data professionals working on data mining at the R&D and implementation levels. Upper-level undergrads and graduate students in data mining at computer science programs https://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1
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Books Books Vikram Sarabhai Library
General Stacks
005.741 H2D2/2012 (Browse shelf) Available 195984
Books Books Vikram Sarabhai Library
650.285 H2D2/2001 (Browse shelf) Checked out to Mohan B Paliwal (RS16) 27/05/2018 150463

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.
Key Features
• 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
Readership
Data warehouse engineers, data mining professionals, database researchers, statisticians, data analysts, data modelers, and other data professionals working on data mining at the R&D and implementation levels. Upper-level undergrads and graduate students in data mining at computer science programs


https://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1

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