W	Lec	Topics Covered
0 23/02	Lec 0	Course Overview Data Deluge and Technical Challenges, Definitions for Data Mining and Related Sciences, Data Science Related Jobs and University Graduate Programs, DIKW Process Examples, Tools and Languages for Data Science, Course Description and Objectives, Requirements and Assumptions, Course Outline, Text Books, Other Lecture Materials and Tools, Course Activities and Grading
1 01/03	Lec 1	Introduction to Data Mining Why Data Mining? What is Data Mining? What Kinds of Data can be Mined? Data Mining Functions Which Sciences and Technologies are Used? Which Kinds of Applications are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society
2 08/03	Lec 2	Getting to Know Your Data Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Measuring Data Similarity and Dissimilarity
3 15/03	Lec 3	Data Preprocessing Data Preprocessing: An Overview, Data Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization
4 22/03	Lab 1	Getting to Know Your Data and Data Preprocessing using WEKA
5 29/03	Lec 4	Association Analysis-Basics Concepts and Methods Introduction, Frequent Itemsets, Scalable Mining Methods and Apriori Algorithm, Finding Association Rules with Apriori, Learning Association Rules using WEKA
6 05/04	Lec 5	Classification: Basic Concepts, Decision Trees, and Model Evaluation Basic Concepts, Decision Tree Based Classification, Practical Issues of Classification, Model Evaluation, Classification using WEKA
7 12/04		Midterm Exam I
8 19/04		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 26/04		Classification-Additional Topics: Other Classification Methods, Model Evaluation and Selection, Techniques to Improve Classification Accuracy: Ensemble Methods
10 03/05		Classification Applications
11 10/05		Cluster Analysis-Basic Concepts and Methods: Cluster Analysis: Basic Concepts, Partitioning Methods, Hierarchical Methods, Density-Based Methods, Grid-Based Methods, Evaluation of Clustering
12 17/05		Cluster Analysis-Advanced Methods: Probability Model-Based Clustering, Clustering High-Dimensional Data, Clustering Graphs and Network Data, Clustering with Constraints
13 24/05		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
14 31/05		Midterm Exam II