Data Mining: Concepts and Techniques: Concepts and Techniques [NOOK Book]


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 ...
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Data Mining: Concepts and Techniques: Concepts and Techniques

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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

"...each chapter functions as a stand-alone guide to a critical topic, presenting proven algorithms & sound implementations ready to be used directly or with strategic modification against live data."

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Product Details

Meet the Author

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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Read an Excerpt

The Preeminent textbook and professional reference on data mining from the recognized authoirty on the subject.
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Table of Contents

Chapter 1 Introduction 

Chapter 2. Getting to Know Your Data 

Chapter 3. Preprocessing: Data Reduction, Transformation, and Integration 

Chapter 4. Data Warehousing and On-Line Analytical Processing 

Chapter 5. Data Cube Technology 

Chapter 6. Mining Frequent Patterns, Associations and Correlations: Concepts and


Chapter 7. Advanced Frequent Pattern Mining 

Chapter 8. Classification: Basic Concepts 

Chapter 9. Classification: Advanced Methods 

Chapter 10. Cluster Analysis: Basic Concepts and Methods 

Chapter 11. Cluster Analysis: Advanced Methods 

Chapter 12. Outlier Analysis 

Chapter 13. Trends and Research Frontiers in Data Mining

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  • Posted September 24, 2011


    Are you a computer science student, application developer, and business professional; as well as, a researcher? If you are, then this book is for you! Authors Jiawei Han, Micheline Kamber, and Jian Pei, have done an outstanding job of writing a third edition of a book which explores the concepts and techniques of knowledge discovery and data mining. Han, Kamber and Pei, begin by providing an introduction to the multidisciplinary field of data mining. In addition, the authors introduce the general data features. The authors then focus on the techniques for data processing. Then, they look at the basic concepts, modeling, design architectures, and general implementations of data warehouses and OLAP; as well as, the relationship between data warehousing and other data generalization methods. Next, the authors take an in-depth look at cube technology, presenting a detailed study of methods of data cube computation, including Star-Cubing and high-dimensional OLAP methods. They continue with an in-depth look at the fundamental concepts, such as market basket analysis, with many techniques for frequent itemset mining presented in an organized way. In addition, the authors discuss methods for pattern mining in multilevel and multidimensional space, mining rare and negative patterns, mining colossal patterns and high-dimensional data, constraint-based pattern mining, and mining compressed or approximate patterns. The authors then introduce the basic concepts and methods for classification, including decision tree induction, Bayes classification, and rule-based classification. Then, they discuss advanced methods for classification, including Bayesian belief networks, the neural network technique of backpropagation , support vector machines, classification using frequent patterns, k-nearest-neighbor classifiers, case-based reasoning, genetic algorithms, rough set theory, and fuzzy set approaches. Next, the authors introduce the basic concepts and methods for data clustering, including an overview of basic cluster analysis methods, partitioning methods, hierarchical methods, density-based methods, and grid-based methods. They continue with a discussion of advanced methods for clustering, including probabilistic model-based clustering, clustering high-dimensional data, clustering graph and network data, and clustering with constraints. In addition, the authors introduce the basic concepts of outliers and outlier analysis, and discuss various outlier detection methods from the view of degree of supervision; as well as, from the view of approaches. Finally, the authors discuss trends, applications, and research frontiers in data mining. This most excellent book is not intended as an introduction to statistics, machine learning, database systems, or other such areas. Rather, the book is a comprehensive introduction to data mining.

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