Korea Advanced Institute of Science and Technology
CS492 Data Mining
Fall 2013 - U Kang

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Course Information

Data are everywhere: examples include the World Wide Web, financial data, social network, mobile call data, biological data, and many more. Data Mining aims to find userful patterns and anomalies which lead to high impact applications including web search, fraud detection, recommendation system, cyber security, etc.
This course covers important algorithms for data mining. Topics include data preprocessing, mining frequent patterns, classification, cluster analysis, stream analysis, outlier analysis, graphs, and mining big data.


Lecture Date Topic Due
1 Sep 2 Introduction (HKP Chapter 1)
2 9 Getting to Know Your Data (HKP Chapter 2) hw1 out
3 16 Data Preprocessing (HKP Chapter 3) hw1 sol; hw2 out
4 23 Data Warehousing and Online Analytical Processing (HKP Chapter 4) hw2 sol; hw3 out
5 30 Data Cube Technology (HKP Chapter 5) hw3 sol; hw4 out
6 Oct 7 Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods (HKP Chapter 6) hw4 sol; hw5 out
7 14 Advanced Pattern Mining (HKP Chapter 7) hw5 sol; hw6 out
21 Midterm week midterm solution
8 28 Classification: Basic Concepts (HKP Chapter 8) hw6 sol; hw7 out
9 Nov 4 Classification: Advanced Methods (HKP Chapter 9) hw7 sol; hw8 out
10 11 Cluster Analysis: Basic Concepts and Methods (HKP Chapter 10) hw8 sol; hw9 out
11 18 Advanced Cluster Analysis (HKP Chapter 11) hw9 sol; hw10 out
12 25 Outlier Detection (HKP Chapter 12) hw10 sol; hw11 out
13 Dec 2 Data Mining Trends and Research Frontiers (HKP Chapter 13) hw11 sol
14 9 No class - prepare your exam
16 Final exam week Final exam guide; final solution


Late policy - for all deliverables:


Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems), by Jiawei Han, Micheline Kamber, Jian Pei.
Last modified September 16, 2013, by U Kang