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Concepts and Techniques
To order this title, and for more information, click here
Second Edition
By
Jiawei Han, University of Illinois, Urbana Champaign
Micheline Kamber, Simon Fraser University, Burnaby, Canada
Jian Pei
Description
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions
now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side,
scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of
data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform
this data into useful information and knowledge.
Like the first edition, voted the most popular data mining book by KD Nuggets readers,
this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their
feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been
made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition,
and new chapters have been added to address recent developments on mining complex types of data? including stream data, sequence data,
graph structured data, social network data, and multi-relational data.
Audience
database professionals and researchers, data mining professionals; undergraduate and graduate students
Contents
Chapter 1: Introduction
1.1 What Motivated Data Mining? Why Is It Important?
1.2 So, What Is Data Mining?
1.3 Data Mining--On
What Kind of Data?
1.4 Data Mining Functionalities?What Kinds of Patterns Can Be Mined?
1.5 Are All of the Patterns Interesting?
1.6
Classification of Data Mining Systems
1.7 Data Mining Task Primitives
1.8 Integration of a Data Mining System with a Database or Data
Warehouse System
1.9 Major Issues in Data Mining
1.10 Summary
1.11 Exercises
1.12 Bibliographic Notes
Chapter 2: Data Preprocessing
2.1 Why Preprocess the Data?
2.2 Descriptive Data Summarization
2.3 Data Cleaning
2.4 Data Integration and Transformation
2.5 Data
Reduction
2.6 Data Discretization and Concept Hierarchy Generation
2.7 Summary
2.8 Exercises
2.9 Bibliographic Notes
Chapter
3: Data Warehouse and OLAP Technology: An Overview
3.1 What Is a Data Warehouse?
3.2 A Multidimensional Data Model
3.3 Data
Warehouse Architecture
3.4 Data Warehouse Implementation
3.5 From Data Warehousing to Data Mining
3.6 Summary
3.7 Exercises
3.8 Bibliographic
Notes
Chapter 4: Data Cube Computation and Data Generalization
4.1 Efficient Methods for Data Cube Computation
4.2
Further Development of Data Cube and OLAP Technology
4.3 Attribute-Oriented Induction?An Alternative Method for Data Generalization
and Concept De-
scription
4.4 Summary
4.5 Exercises
4.6 Bibliographic Notes
Chapter 5: Mining Frequent Patterns, Associations,
and Correlations
5.1 Basic Concepts and a Road Map
5.2 Efficient and Scalable Frequent Itemset Mining Methods
5.3 Mining Various
Kinds of Association Rules
5.4 From Association Mining to Correlation Analysis
5.5 Constraint-Based Association Mining
5.6 Summary
5.7 Exercises
5.8 Bibliographic Notes
Chapter 6: Classification and Prediction
6.1 What Is Classification? What
Is Prediction?
6.2 Issues Regarding Classification and Prediction
6.3 Classification by Decision Tree Induction
6.4 Bayesian Classification
6.5 Rule-Based Classification
6.6 Classification by Backpropagation
6.7 Support Vector Machines
6.8 Associative Classification: Classification
by Association Rule Analysis
6.9 Lazy Learners (or Learning from Your Neighbors)
6.10 Other Classification Methods
6.11 Prediction
6.12 Accuracy and Error Measures
6.13 Evaluating the Accuracy of a Classifier or Predictor
6.14 Ensemble Methods?Increasing the Accuracy
6.15 Model Selection
6.16 Summary
6.17 Exercises
6.18 Bibliographic Notes
Chapter 7: Cluster Analysis
7.1 What
Is Cluster Analysis?
7.2 Types of Data in Cluster Analysis
7.3 A Categorization of Major Clustering Methods
7.4 Partitioning Methods
7.5 Hierarchical Methods
7.6 Density-Based Methods
7.7 Grid-Based Methods
7.8 Model-Based Clustering Methods
7.9 Clustering High-Dimensional
Data
7.10 Constraint-Based Cluster Analysis
7.11 Outlier Analysis
7.12 Summary
7.13 Exercises
7.14 Bibliographic Notes
Chapter
8: Mining Stream, Time-Series, and Sequence Data
8.1 Mining Data Streams
8.2 Mining Time-Series Data
8.3 Mining Sequence Patterns
in Transactional Databases
8.4 Mining Sequence Patterns in Biological Data
8.5 Summary
8.6 Exercises
8.7 Bibliographic Notes
Chapter
9: Graph Mining, Social Network Analysis, and Multi-Relational Data Mining
9.1 Graph Mining
9.2 Social Network Analysis
9.3
Multi-Relational Data Mining
9.4 Summary
9.5 Exercises
9.6 Bibliographic Notes
Chapter 10: Mining Object, Spatial, Multimedia,
Text, and Web Data
10.1 Multidimensional Analysis and Descriptive Mining of Complex Data Objects
10.2 Spatial Data Mining
10.3 Multimedia Data Mining
10.4 Text Mining
10.5 Mining the World Wide Web
10.6 Summary
10.7 Exercises
10.8 Bibliographic Notes
Chapter 11 Applications and Trends in Data Mining
11.1 Data Mining Applications
11.2 Data Mining System Products
and Research Prototypes
11.3 Additional Themes on Data Mining
11.4 Social Impacts of Data Mining
11.5 Trends in Data Mining
11.6
Summary
11.7 Exercises
11.8 Bibliographic Notes
Appendix A: An Introduction to Microsoft's OLE DB for Data Mining
| Bibliographic details |
Hardbound, 800 pages, publication date: MAR-2006
ISBN-13: 978-1-55860-901-3
ISBN-10: 1-55860-901-6
Imprint: MORGAN KAUFFMAN
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Last update: 7 Sep 2009
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