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1. MSCS
Data Mining
Week 1. Introduction
 Why Data Mining?
 What Is Data Mining?
 A Multi-Dimensional View of Data Mining
 What Kind of Data Can Be Mined?
 What Kinds of Patterns Can Be Mined?
 What Technology Are Used?
 What Kind of Applications Are Targeted?
 Major Issues in Data Mining
 A Brief History of Data Mining and Data Mining Society
 Summary
Week 2. Know Your Data
 Data Objects and Attribute Types
 Basic Statistical Descriptions of Data
 Data Visualization
 Measuring Data Similarity and Dissimilarity
 Summary
Week 3. Data Preprocessing
 Data Preprocessing: An Overview
 Data Quality
 Major Tasks in Data Preprocessing
 Data Cleaning
 Data Integration
 Data Reduction
 Data Transformation and Data Discretization
 Summary
Week 4. Data Warehousing and On-Line Analytical Processing
 Data Warehouse: Basic Concepts
 Data Warehouse Modeling: Data Cube and OLAP
 Data Warehouse Design and Usage
 Data Warehouse Implementation
 Data Generalization by Attribute-Oriented Induction
 Summary
Week 5. Data Cube Technology
 Data Cube Computation: Preliminary Concepts
 Data Cube Computation Methods
 Processing Advanced Queries by Exploring Data Cube Technology
 Multidimensional Data Analysis in Cube Space
 Summary

2. Week 6. Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods
 Basic Concepts
 Frequent Itemset Mining Methods
 Which Patterns Are Interesting?—Pattern Evaluation Methods
 Summary
Week 7. Advanced Frequent Pattern Mining
 Pattern Mining: A Road Map
 Pattern Mining in Multi-Level, Multi-Dimensional Space
 Constraint-Based Frequent Pattern Mining
 Mining High-Dimensional Data and Colossal Patterns
 Mining Compressed or Approximate Patterns
 Pattern Exploration and Application
 Summary
Week 8. Classification: Basic Concepts
 Classification: Basic Concepts
 Decision Tree Induction
 Bayes Classification Methods
 Rule-Based Classification
 Model Evaluation and Selection
 Techniques to Improve Classification Accuracy: Ensemble Methods
 Summary
Week 9. Classification: Advanced Methods
 Bayesian Belief Networks
 Classification by Backpropagation
 Support Vector Machines
 Classification by Using Frequent Patterns
 Lazy Learners (or Learning from Your Neighbors)
 Other Classification Methods
 Additional Topics Regarding Classification
 Summary
Week 10. Cluster Analysis: Basic Concepts and Methods
 Cluster Analysis: Basic Concepts
 Partitioning Methods
 Hierarchical Methods
 Density-Based Methods
 Grid-Based Methods
 Evaluation of Clustering
 Summary
Week 11. Cluster Analysis: Advanced Methods
 Cluster Analysis: Basic Concepts
 Group data so that object similarity is high within clusters but low across clusters
 Partitioning Methods
 K-means and k-medoids algorithms and their refinements
 Hierarchical Methods
 Agglomerative and divisive method, Birch, Cameleon
 Density-Based Methods

3.  DBScan, Optics and DenCLu
 Grid-Based Methods
 STING and CLIQUE (subspace clustering)
 Evaluation of Clustering
 Assess clustering tendency, determine # of clusters, and measure clustering quality
Week 12. Outlier Detection
 Outlier and Outlier Analysis
 Outlier Detection Methods
 Statistical Approaches
 Proximity-Base Approaches
 Clustering-Base Approaches
 Classification Approaches
 Mining Contextual and Collective Outliers
 Outlier Detection in High Dimensional Data
 Summary
Week 13. Trends and Research Frontiers in Data Mining
Recommended Books
 Mining Complex Types of Data
 Other Methodologies of Data Mining
 Data Mining Applications
 Data Mining and Society
 Data Mining Trends
 Summary
1. Data Mining: Concepts and Techniques, 3rd
ed.
By Jiawei Han, Micheline Kamber and Jian Pei
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
2. Data Mining: Introductory and Advanced Topics1
By Margaret H. Dunham