Download CS 668 Advanced Topics in Database Technologies

CS 668 Advanced Topics in Database Technologies
Course Hours: Tuesday 6:00 ~ 8;35 PM Spring 2013 Classroom: LLC
207 (Cook Lab)
Textbooks of CS 668:
(Required) Data Mining, Concepts and Technologies, 3rd Edition,
Jiawei Han, Micheline Kamber, and Jian Pei
The Morgan Kaufmann, (Series in Data Management systems), ISBN
978-0-12-381479-1, 2011.
Useful Web Resources:
1. http://www.cs.uiuc.edu/~hanj/
2. Modern Data Warehousing, Mining, and Visualization – Core
Concepts, Prentice-Hall.
3. Practical Applications of Data Mining, Sang C. Sug
Prerequisite of CS 668: CS 649 Database Management Systems.
Instructor: Prof. Ping-Tsai Chung
Contact Information: Office: LLC 206P E-mail: [email protected] Tel:
(718) 488-1073
Office Hours: Tuesday & Wednesday 5:00 - 6:00 PM (LLC 203) or by
appointment.
Participation/Course Grade: Assignments & Project(s): 70%, Exam:
30%
Approximate Schedule of Topics: Approximate Schedule of Topics:
Reading
Schedule
Topics Covered
Assignments
1
Introduction –
1. What Motivated Data Mining? Why Is It
Important?
2 So, What Is Data Mining?
3 Data Mining--On What Kind of Data?
4 Data Mining Functionalities-What Kinds of Patterns
Can Be Mined?
Chapter 1 & Notes
5 Are All of the Patterns Interesting?
6 Classification of Data Mining Systems
7 Data Mining Task Primitives
8 Integration of a Data Mining System with a
Database or Data Warehouse System
9 Major Issues in Data Mining
2
Getting to Know Your Data -
Chapter 2 & Notes
1. Types of Data Sets and Attribute Values
2. Basic Statistical Descriptions of Data
3. Data Visualization
4. Measuring Data Similarity
3
Preprocessing -
Chapter 3 & Notes
1. Data Quality
2. Major Tasks in Data Preprocessing
3. Data Reduction
4. Data Transformation and Data Discretization
5. Data Cleaning and Data Integration
4
Data Warehousing and On-Line Analytical Ch 4 & Notes
Processing 1. Data Warehouse: Basic Concepts
2. Data Warehouse Modeling: Data Cube and OLAP
3. Data Warehouse Design and Usage
4. Data Warehouse Implementation
5. Data Generalization by Attribute-Oriented
Induction
5
Mining Frequent Patterns, Associations
and Correlations: Concepts and
Methods -
Chapter 6 & Notes
1. Basic Concepts
2. Frequent Itemset Mining Methods
3. Which Patterns Are Interesting? Pattern Evaluation
Methods
4. Association Rules – Notes -
6
Classification Learning: Basic Concepts -
Chapter 8 & Notes
1. Classification Learning: Basic Concepts
2. Decision Tree Induction
3. Rough Sets & Bayes Theories – Notes 4. Rule-Based Classification
5. Model Evaluation and Selection
6. Techniques to Improve Classification Accuracy:
Ensemble Methods
7
Cluster Analysis: Basic Concepts and
Methods -
Chapter 10 & Notes
1. Cluster Analysis: Basic Concepts
2. Clustering structures
3. Major Clustering Approaches
4. Partitioning Methods
5. Hierarchical Methods
6. Graph Theory Algorithm with the Single-link
Method - Notes 7. Density-Based Methods
8
8. Association Rule Algorithm - Notes Trends and Research Frontiers in Data Mining -
Chapter 13 & Notes
1. Mining Complex Types of Data
2. Advanced Data Mining Applications
3. Data Mining System Products and Research
Prototypes
4. Social Impacts of Data Mining
5. Trends in Data Mining
9
PROJECT PRESENTATIONS
10
FINAL EXAM – Contents will be discussed in the
Class