PRESCRIBED TEXT FOR COMP3420 + COMP8400 AT ANU, SEMESTER 1 2011Our 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
Contents
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
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
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
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
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
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
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
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
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
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
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
