Data Mining: Concepts and Techniques, 3rd Edition
July 12, 2011
[MK] Sloane
 |
Data Mining: Concepts and Techniques, 3rd Edition by Jiawei Han , Micheline Kamber and Jian Pei just published! The full Table of Contents is listed below. |
About the Book:
The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it’s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.
Since the previous edition’s publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today’s most powerful data mining techniques to meet real business challenges.
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
Reviews:
We are living in the data deluge age. Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. This third edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The book also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. With its companion website, it 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)…. 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
Table of Contents
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 DataWarehousing and Online Analytical Processing
4.1 DataWarehouse: Basic Concepts
4.2 DataWarehouse Modeling: Data Cube and OLAP
4.3 DataWarehouse Design and Usage
4.4 DataWarehouse 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
ISBN: 9780123814791 | View in bookstore
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