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Data mining : concepts and techniques

Author: Jiawei Han; Micheline Kamber; Jian Pei, (Computer scientist)
Publisher: Waltham, MA : Morgan Kaufmann/Elsevier, ©2012.
Series: Morgan Kaufmann series in data management systems.
Edition/Format:   eBook : Document : English : 3rd edView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Han, Jiawei.
Data mining.
Amsterdam ; Boston : Elsevier/Morgan Kaufmann, ©2012
(DLC) 2011010635
(OCoLC)711777246
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jiawei Han; Micheline Kamber; Jian Pei, (Computer scientist)
ISBN: 0123814790 9780123814791 9780123814807 0123814804
OCLC Number: 795224972
Description: 1 online resource (xxxv, 703 pages) : illustrations, facsimiles.
Contents: Front Cover --
Data Mining: Concepts and Techniques --
Copyright --
Dedication --
Table of Contents --
Foreword --
Foreword to Second Edition --
Preface --
Acknowledgments --
About the Authors --
Chapter 1. Introduction --
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 --
Chapter 2. Getting to Know Your Data --
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 --
Chapter 3. Data Preprocessing --
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 --
Chapter 4. Data Warehousing and Online Analytical Processing --
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 --
4.8 Bibliographic Notes --
Chapter 5. Data Cube Technology --
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 --
Chapter 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods --
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 --
Chapter 7. Advanced Pattern Mining --
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 --
Chapter 8. Classification: Basic Concepts --
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 --
Chapter 9. Classification: Advanced Methods --
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 --
9.8 Summary --
9.9 Exercises --
9.10 Bibliographic Notes --
Chapter 10. Cluster Analysis: Basic Concepts and Methods --
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 --
Chapter 11. Advanced Cluster Analysis --
11.1 Probabilistic Model-Based Clustering --
11.2 Clustering High-Dimensional Data --
11.3 Clustering Graph and Network Data --
11.4 Clustering with Constraints --
11.5 Summary --
11.6 Exercises --
11.7 Bibliographic Notes --
Chapter 12. Outlier Detection --
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 --
Chapter 13. Data Mining Trends and Research Frontiers --
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.
Series Title: Morgan Kaufmann series in data management systems.
Responsibility: Jiawei Han, Micheline Kamber, Jian Pei.

Abstract:

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.

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