Data Mining: Concepts and Techniques, 3rd Edition, Author(s): Jiawei Han, Micheline Kamber, Jian Pei, Published: June, 2011 ISBN: 9780123814791, Morgan Kaufmann, Hardcover

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Data Mining: Concepts and Techniques, 3rd Edition, Author(s): Jiawei Han, Micheline Kamber, Jian Pei, Published: June, 2011 ISBN: 9780123814791, Morgan Kaufmann, Hardcover

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Product Details

 

Series: The Morgan Kaufmann Series in Data Management Systems

Hardcover: 744 pages

Publisher: Morgan Kaufmann; 3 edition (June, 2011)

Language: English

ISBN-10: 0123814790

ISBN-13: 978-0123814791

Product Dimensions: 9.5 x 7.7 x 1.8 inches

Shipping Weight: 3.2 pounds

 

Key Features

 

* 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

Description


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.



Readership

 

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.

 

Table of Contents

 

Data Mining: Concepts and Techniques, 3rd Edition

 

 

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

Exercises

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

Exercises

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

Exercises

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

Exercises

Bibliographic Notes

 

Chapter 5. Data Cube Technology

1. Efficient Methods for Data Cube Computation

2. Exploration and Discovery in Multidimensional Databases

3.. Summary

Exercises

Bibliographic Notes

 

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

Methods

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

Exercises

 

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

Exercises

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

Exercises

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

Exercises

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

Exercises

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

Exercises

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

Exercises

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

Exercises

Bibliographic Notes


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

 


 


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