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Part One Introduction
1 Introduction
1.1 Basic Data Mining Tasks
1.2 Data Mining Versus Knowledge Discovery in Databases
1.3 Data Mining Issues
1.4 Data Mining Metrics
1.5 Social Implications of Data Mining
1.6 Data Mining from a Database Perspective
1.7 The Future
1.8 Exercises
1.9 Bibliographic Notes
2 Related Concepts
2.1 Database/OLTP Systems
2.2 Fuzzy Sets and Fuzzy Logic
2.3 Information Retrieval
2.4 Decison Support Systems
2.5 Dimensional Modeling
2.6 Indexing
2.7 Data Warehousing
2.8 OLAP
2.9 Web Search Engines
2.10 Statistics
2.11 Machine Learning
2.12 Summary
2.13 Exercises
2.14 Bibliographic Notes
3 Data Mining Technques
3.1 Introduction
3.2 A Statistical Perspective on Data Mining
3.3 Similarity Measures
3.4 Decision Trees
3.5 Neural Networks
3.6 Genetic Algorithms
3.7 Exercises
3.8 Bibliographic Notes
Part Two Core Topics
4 Classification
4.1 Introduction
4.2 Statistical-Based Algorthms
4.3 Dstance-Baesd Algorithms
4.4 Decision Tree-Based Algouithms
4.5 Neural Network-Based Algorithms
4.6 Rule-Based Algorithms
4.7 Combining Techniques
4.8 Summary
4.9 Exercises
4.10 Bibliographic Notes
5 Chustering
5.1 Introduction
5.2 Similarity and Distance Measures
5.3 Outliers
5.4 Hierarchcal Algorithms
5.5 Partitional Algorithms
5.6 Clustering Large Databases
5.7 Clustering with CATEGOUICAL attrebutes
5.8 Comparises
5.9 Exercise
5.10 Bibliographic Notes
6 Association Rules
6.1 Intrdrction
6.2 Large Itemsets
6.3 Basic Algorithms
6.4 Parallel and Distributed Algorithms
6.5 Comparing Apprlaches
6.6 Incremental Rules
6.7 Advanced Association Rule Techniques
6.8 Measuring the Quality of Rules
6.9 Exercises
6.10 Bibliographic Notes
Part Three Advanced Topics
7 Web Mining
7.1 Introduction
7.2 Web Content Mining
7.3 Web Structure Mining
7.4 Web Usage Mining
7.5 Exercises
7.6 Bibliographic Notes
8 Spatial Mining
8.1 Introduction
8.2 Spatial Data Overview
8.3 Spatial Data Mining Primitives
8.4 Generalization and Specialization
8.5 Spatial Rules
8.6 Spatial Classification Algouithm
8.7 Spatial Clustering Algorithms
8.8 Exercises
8.9 Bibliographic Notes
9 Temporal Mining
9.1 Introduction
9.2 Modeling Temporal Events
9.3 Time Series
9.4 Pattern Detcdtion
9.5 Sequences
9.6 Temporal Association Rules
9.7 Exercises
9.8 Bibliographic Notes
APPENDICES
A Data Mining Products
A.1 Bibliographic Notes
B Bibliography
Index
About the Author