Data Mining: Concepts and Techniques: Concepts and Techniques (Google eBook)

Front Cover
Elsevier, Jun 9, 2011 - Computers - 744 pages
3 Reviews
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

      

    What people are saying - Write a review

    Review: Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

    User Review  - Mona Mahfouz - Goodreads

    Read for Data Mining course. Well written and easy to follow with good examples. Read full review

    Review: Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

    User Review  - Radek Lát - Goodreads

    A good collection of data mining techniques. However, for actual implementation of the presented algorithms you might need to look somewhere else because the presented information is not always clear and the examples are often difficult to transform to your own problems. Read full review

    Contents

    Chapter 1 Introduction
    1
    Chapter 2 Getting to Know Your Data
    39
    Chapter 3 Data Preprocessing
    83
    Chapter 4 Data Warehousing and Online Analytical Processing
    125
    Chapter 5 Data Cube Technology
    187
    Basic Concepts and Methods
    243
    Chapter 7 Advanced Pattern Mining
    279
    Basic Concepts
    327
    Advanced Methods
    393
    Basic Concepts and Methods
    443
    Chapter 11 Advanced Cluster Analysis
    497
    Chapter 12 Outlier Detection
    543
    Chapter 13 Data Mining Trends and Research Frontiers
    585
    Bibliography
    633
    Index
    673
    Copyright

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    About the author (2011)

    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 Associate Professor of Computing Science and the director of Collaborative Research and Industry Relations at the School of Computing Science at Simon Fraser University, Canada. In 2002-2004, he was an Assistant Professor of Computer Science and Engineering at the State University of New York (SUNY) at Buffalo. He received a Ph.D. degree in Computing Science from Simon Fraser University in 2002, under Dr. Jiawei Han's supervision.

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