W	D	Lec	Topics Covered
1	14/02		Introduction: An Overview of Data Mining
2	21/02		Getting to Know Your Data: Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Measuring Data Similarity and Dissimilarity
3	28/02		Data Preprocessing (1/2): Data Preprocessing: An Overview: Data Quality, Major Tasks in Data Preprocessing, Data Cleaning, Data Integration
4	06/03		Data Preprocessing (2/2): Data Reduction, Data Transformation and Data Discretization
5	13/03		Mining Frequent Patterns, Association and Correlations-Basic Concepts and Methods: Basic Concepts, Frequent Itemset Mining Methods, Which Patterns are Interesting?—Pattern Evaluation Methods
6	20/03		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
7	27/03		Classification-Basic Concepts: Classification: Basic Concepts, Decision Tree Induction
8	03/04		Classification-Advanced Methods: Rule-Based Classification, Bayes Classification Methods, Neural Networks and Classification by Backpropagation, Support Vector Machines, Classification by Using Frequent Patterns, Lazy Learners (or Learning from Your Neighbors)
9	10/04		Classification-Additional Topics: Other Classification Methods, Model Evaluation and Selection, Techniques to Improve Classification Accuracy: Ensemble Methods
10	17/04		Cluster Analysis-Basic Concepts and Methods: Cluster Analysis: Basic Concepts, Partitioning Methods, Hierarchical Methods, Density-Based Methods, Grid-Based Methods, Evaluation of Clustering
11	24/04		Cluster Analysis-Advanced Methods: Probability Model-Based Clustering, Clustering High-Dimensional Data, Clustering Graphs and Network Data, Clustering with Constraints
12	01/05		Outlier Analysis: 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
13	08/05		Project Demonstrations 1
14	15/05		Project Demonstrations 2