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Data Mining: Concepts and Techniques
- 3rd Edition - June 9, 2011
- Authors: Jiawei Han, Micheline Kamber, Jian Pei
- Language: English
- eBook ISBN:9 7 8 - 0 - 1 2 - 3 8 1 4 8 0 - 7
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifica… Read more
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Request a sales quoteData 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
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
- Dedication
- Foreword
- Foreword to Second Edition
- Preface
- Organization of the Book
- To the Instructor
- To the Student
- To the Professional
- Book Web Sites with Resources
- Acknowledgments
- Third Edition of the Book
- Second Edition of the Book
- First Edition of the Book
- About the Authors
- 1. Introduction
- Publisher Summary
- 1.1 Why Data Mining?
- 1.2 What Is Data Mining?
- 1.3 What Kinds of Data Can Be Mined?
- 1.4 What Kinds of Patterns Can Be Mined?
- 1.5 Which Technologies Are Used?
- 1.6 Which Kinds of Applications Are Targeted?
- 1.7 Major Issues in Data Mining
- 1.8 Summary
- 1.9 Exercises
- 1.10 Bibliographic Notes
- 2. Getting to Know Your Data
- Publisher Summary
- 2.1 Data Objects and Attribute Types
- 2.2 Basic Statistical Descriptions of Data
- 2.3 Data Visualization
- 2.4 Measuring Data Similarity and Dissimilarity
- 2.5 Summary
- 2.6 Exercises
- 2.7 Bibliographic Notes
- 3. Data Preprocessing
- Publisher Summary
- 3.1 Data Preprocessing: An Overview
- 3.2 Data Cleaning
- 3.3 Data Integration
- 3.4 Data Reduction
- 3.5 Data Transformation and Data Discretization
- 3.6 Summary
- 3.7 Exercises
- 3.8 Bibliographic Notes
- 4. Data Warehousing and Online Analytical Processing
- Publisher Summary
- 4.1 Data Warehouse: Basic Concepts
- 4.2 Data Warehouse Modeling: Data Cube and OLAP
- 4.3 Data Warehouse Design and Usage
- 4.4 Data Warehouse Implementation
- 4.5 Data Generalization by Attribute-Oriented Induction
- 4.6 Summary
- 4.7 Exercises
- Bibliographic Notes
- 5. Data Cube Technology
- Publisher Summary
- 5.1 Data Cube Computation: Preliminary Concepts
- 5.2 Data Cube Computation Methods
- 5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology
- 5.4 Multidimensional Data Analysis in Cube Space
- 5.5 Summary
- 5.6 Exercises
- 5.7 Bibliographic Notes
- 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
- Publisher Summary
- 6.1 Basic Concepts
- 6.2 Frequent Itemset Mining Methods
- 6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods
- 6.4 Summary
- 6.5 Exercises
- 6.6 Bibliographic Notes
- 7. Advanced Pattern Mining
- Publisher Summary
- 7.1 Pattern Mining: A Road Map
- 7.2 Pattern Mining in Multilevel, Multidimensional Space
- 7.3 Constraint-Based Frequent Pattern Mining
- 7.4 Mining High-Dimensional Data and Colossal Patterns
- 7.5 Mining Compressed or Approximate Patterns
- 7.6 Pattern Exploration and Application
- 7.7 Summary
- 7.8 Exercises
- 7.9 Bibliographic Notes
- 8. Classification: Basic Concepts
- Publisher Summary
- 8.1 Basic Concepts
- 8.2 Decision Tree Induction
- 8.3 Bayes Classification Methods
- 8.4 Rule-Based Classification
- 8.5 Model Evaluation and Selection
- 8.6 Techniques to Improve Classification Accuracy
- 8.7 Summary
- 8.8 Exercises
- 8.9 Bibliographic Notes
- 9. Classification: Advanced Methods
- Publisher Summary
- 9.1 Bayesian Belief Networks
- 9.2 Classification by Backpropagation
- 9.3 Support Vector Machines
- 9.4 Classification Using Frequent Patterns
- 9.5 Lazy Learners (or Learning from Your Neighbors)
- 9.6 Other Classification Methods
- 9.7 Additional Topics Regarding Classification
- Summary
- 9.9 Exercises
- 9.10 Bibliographic Notes
- 10. Cluster Analysis: Basic Concepts and Methods
- Publisher Summary
- 10.1 Cluster Analysis
- 10.2 Partitioning Methods
- 10.3 Hierarchical Methods
- 10.4 Density-Based Methods
- 10.5 Grid-Based Methods
- 10.6 Evaluation of Clustering
- 10.7 Summary
- 10.8 Exercises
- 10.9 Bibliographic Notes
- 11. Advanced Cluster Analysis
- Publisher Summary
- 11.1 Probabilistic Model-Based Clustering
- 11.2 Clustering High-Dimensional Data
- 11.3 Clustering Graph and Network Data
- 11.4 Clustering with Constraints
- Summary
- 11.6 Exercises
- 11.7 Bibliographic Notes
- 12. Outlier Detection
- Publisher Summary
- 12.1 Outliers and Outlier Analysis
- 12.2 Outlier Detection Methods
- 12.3 Statistical Approaches
- 12.4 Proximity-Based Approaches
- 12.5 Clustering-Based Approaches
- 12.6 Classification-Based Approaches
- 12.7 Mining Contextual and Collective Outliers
- 12.8 Outlier Detection in High-Dimensional Data
- 12.9 Summary
- 12.10 Exercises
- 12.11 Bibliographic Notes
- 13. Data Mining Trends and Research Frontiers
- Publisher Summary
- 13.1 Mining Complex Data Types
- 13.2 Other Methodologies of Data Mining
- 13.3 Data Mining Applications
- 13.4 Data Mining and Society
- 13.5 Data Mining Trends
- 13.6 Summary
- 13.7 Exercises
- 13.8 Bibliographic Notes
- Bibliography
- Index
- No. of pages: 744
- Language: English
- Edition: 3
- Published: June 9, 2011
- Imprint: Morgan Kaufmann
- eBook ISBN: 9780123814807
JH
Jiawei Han
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.
Affiliations and expertise
Professor, Department of Computer ScienceUniversity of Illinois, Urbana Champaign, USAMK
Micheline Kamber
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.
Affiliations and expertise
Simon Fraser University, Burnaby, CanadaJP
Jian Pei
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.
Affiliations and expertise
Simon Fraser University, Burnaby, CanadaRead Data Mining: Concepts and Techniques on ScienceDirect