Data Mining: Concepts and Techniques


  • Jiawei Han, University of Illinois, Urbana Champaign
  • Micheline Kamber, Simon Fraser University, Burnaby, Canada
  • Jian Pei, Simon Fraser University, Burnaby, Canada

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.
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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. And upper-level undergrads and graduate students in data mining at computer science programs.


Book information

  • Published: June 2011
  • ISBN: 978-0-12-381479-1


"[A] well-written textbook (2nd ed., 2006; 1st ed., 2001) on data mining or knowledge discovery. The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners. The focus is data-all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. Summing Up: Highly recommended. Upper-division undergraduates through professionals/practitioners."--CHOICE

"This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers."--ACM’s Computing

We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses.--Gregory Piatetsky, President, KDnuggets

Jiawei, Micheline, and Jian give an encyclopaedic coverage of all the related methods, from the classic topics of clustering and classification, to database methods (association rules, data cubes) to more recent and advanced topics (SVD/PCA , wavelets, support vector machines)├ó┬Ç┬Ž. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book.-From the foreword by Christos Faloutsos, Carnegie Mellon University

"A very good textbook on data mining, this third edition reflects the changes that are occurring in the data mining field. It adds cited material from about 2006, a new section on visualization, and pattern mining with the more recent cluster methods. It├ó┬Ç┬Ös a well-written text, with all of the supporting materials an instructor is likely to want, including Web material support, extensive problem sets, and solution manuals. Though it serves as a data mining text, readers with little experience in the area will find it readable and enlightening. That being said, readers are expected to have some coding experience, as well as database design and statistics analysis knowledge├ó┬Ç┬ŽTwo additional items are worthy of note: the text├ó┬Ç┬Ös bibliography is an excellent reference list for mining research; and the index is very complete, which makes it easy to locate information. Also, researchers and analysts from other disciplines--for example, epidemiologists, financial analysts, and psychometric researchers--may find the material very useful."--Computing Reviews

"Han (engineering, U. of Illinois-Urbana-Champaign), Micheline Kamber, and Jian Pei (both computer science, Simon Fraser U., British Columbia) present a textbook for an advanced undergraduate or beginning graduate course introducing data mining. Students should have some background in statistics, database systems, and machine learning and some experience programming. Among the topics are getting to know the data, data warehousing and online analytical processing, data cube technology, cluster analysis, detecting outliers, and trends and research frontiers. Chapter-end exercises are included."--SciTech Book News

"This book is an extensive and detailed guide to the principal ideas, techniques and technologies of data mining. The book is organised in 13 substantial chapters, each of which is essentially standalone, but with useful references to the book’s coverage of underlying concepts. A broad range of topics are covered, from an initial overview of the field of data mining and its fundamental concepts, to data preparation, data warehousing, OLAP, pattern discovery and data classification. The final chapter describes the current state of data mining research and active research areas."

Table of Contents

Chapter 1. Introduction

1 What Motivated Data Mining? Why Is It Important?

2 So, What Is Data Mining?

3 Data Mining--On What Kind of Data?

4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined?

5 Are All of the Patterns Interesting?

6 Classification of Data Mining Systems

7 Data Mining Task Primitives

8 Integration of a Data Mining System with a Database or Data Warehouse System

9 Major Issues in Data Mining

10 Summary


Bibliographic Notes

Chapter 2. Getting to Know Your Data

1. Types of Data Sets and Attribute Values

2. Basic Statistical Descriptions of Data

3. Data Visualization

4. Measuring Data Similarity

5. Summary


Bibliographic Notes

Chapter 3. Preprocessing

1. Data Quality

2. Major Tasks in Data Preprocessing

3. Data Reduction

4. Data Transformation and Data Discretization

5. Data Cleaning and Data Integration

6. Summary


Bibliographic Notes

Chapter 4. Data Warehousing and On-Line Analytical Processing

1. Data Warehouse: Basic Concepts

2. Data Warehouse Modeling: Data Cube and OLAP

3. Data Warehouse Design and Usage

4. Data Warehouse Implementation

5. Data Generalization by Attribute-Oriented Induction

6. Summary


Bibliographic Notes

Chapter 5. Data Cube Technology

1. Efficient Methods for Data Cube Computation

2. Exploration and Discovery in Multidimensional Databases

3.. Summary


Bibliographic Notes

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


1. Basic Concepts

2. E±cient and Scalable Frequent Itemset Mining Methods

3. Are All the Pattern Interesting?|Pattern Evaluation Methods

4. Applications of frequent pattern and associations

5. Summary


Chapter 7. Advanced Frequent Pattern Mining

1. Frequent Pattern and Association Mining: A Road Map

2. Mining Various Kinds of Association Rules

3. Constraint-Based Frequent Pattern Mining

4. Extended Applications of Frequent Patterns

5. Summary


Bibliographic Notes

Chapter 8. Classification: Basic Concepts

1. Classification: Basic Concepts

2. Decision Tree Induction

3. Bayes Classi¯cation Methods

4. Rule-Based Classi¯cation

5. Model Evaluation and Selection

6. Techniques to Improve Classi¯cation Accuracy: Ensemble Methods

7. Handling Di®erent Kinds of Cases in Classi¯cation

8. Summary


Bibliographic Notes

Chapter 9. Classification: Advanced Methods

1. Bayesian Belief Networks

2. Classi¯cation by Neural Networks

3. Support Vector Machines

4. Pattern-Based Classi¯cation

5. Lazy Learners (or Learning from Your Neighbors)

6. Other Classi¯cation Methods

7. Summary


Bibliographic Notes

Chapter 10. Cluster Analysis: Basic Concepts and Methods

1. Cluster Analysis: Basic Concepts

2. Clustering structures

3. Major Clustering Approaches

4. Partitioning Methods

5. Hierarchical Methods

6. Density-Based Methods

7. Model-Based Clustering: The Expectation-Maximization Method

8. Other Clustering Techniques

9. Summary


Bibliographic Notes

Chapter 11. Advanced Cluster Analysis

1. Clustering High-Dimensional Data

2. Constraint-Based and User-Guided Cluster Analysis

3. Link-Based Cluster Analysis

4. Semi-Supervised Clustering and Classi¯cation

5. Bi-Clustering

6. Collaborative ¯ltering

7. Summary


Bibliographic Notes

Chapter 12. Outlier Analysis

1. Why outlier analysis? Identifying and handling of outliers

2. Distribution-Based Outlier Detection: A Statistics-Based Approach

3. Classi¯cation-Based Outlier Detection

4. Clustering-Based Outlier Detection

5. Deviation-Based Outlier Detection

6. Isolation-Based Method: From Isolation Tree to Isolation Forest

7. Summary


Bibliographic Notes

Chapter 13. Trends and Research Frontiers in Data Mining

1. Mining Complex Types of Data

2. Advanced Data Mining Applications

3. Data Mining System Products and Research Prototypes

4. Social Impacts of Data Mining

5. Trends in Data Mining

6. Summary


Bibliographic Notes

Appendix A: An Introduction to Microsoft's OLE DB for Data Mining