Table of Contents
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
2.1 Data Objects and Attribute Types
2.2 Basic Statistical Descriptions of Data
2.4 Measuring Data Similarity and Dissimilarity
3.1 Data Preprocessing: An Overview
3.5 Data Transformation and Data Discretization
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
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
6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
6.2 Frequent Itemset Mining Methods
6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods
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
8. Classification: Basic Concepts
8.3 Bayes Classification Methods
8.5 Model Evaluation and Selection
8.6 Techniques to Improve Classification Accuracy
9. Classification: Advanced Methods
9.2 Classification by Backpropagation
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
10. Cluster Analysis: Basic Concepts and Methods
11.1 Probabilistic Model-Based Clustering
11.2 Clustering High-Dimensional Data
11.3 Clustering Graph and Network Data
11.4 Clustering with Constraints
12.1 Outliers and Outlier Analysis
12.2 Outlier Detection Methods
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
13. Data Mining Trends and Research Frontiers
13.1 Mining Complex Data Types