Korea Advanced Institute of Science and Technology
CS665 Advanced Data Mining
Spring 2014 - U Kang

News and Announcements

Course Information

Big data are everywhere: examples include the World Wide Web, social network, mobile call network, biological network, and many more. Mining big data helps us find userful patterns and anomalies which lead to high impact applications including fraud detection, recommendation system, cyber security, etc.
The course covers advanced algorithms for mining and managing big data. Topics include MapReduce/Hadoop, approximation, graph compression, power laws, community detection, graph structure analysis, triangle counting, link analysis, spectral graph analysis, tensor analysis, and anomaly detection.

Schedule

Lecture Date Topic Due
1 Mar 3 Course Introduction
2 5 Graph-1: basics and diameter
(Optional Reading)
3 10 Guest Lecture: Graph Triangulation by Hamyung Park
4 12 Guest Lecture: Graph Compression by Yongsub Lim
5 17 Graph-2: models
(Optional Reading)
6 19 Graph-3: power law
(Optional Reading)
7 24 Graph-4: structure analysis
(Optional Reading)
8 26 Spectral analysis-1: random walk
(Optional Reading)
9 31 Spectral analysis-2: link analysis
(Mandatory Reading) (Optional Reading)
hw1 due
Proposal due (10:29 am)
Apr 2 Project proposal presentation.
10 7 Spectral analysis-3: link prediction
(Mandatory Reading) (Optional Reading)
hw2 due
11 9 Spectral Analysis-4: triangle counting
(Mandatory Reading) (Optional Reading)
hw3 due
12 14 MapReduce-1: architecture
(Mandatory Reading)
hw4 due
13 16 MapReduce-2: data mining algorithms
(Optional Reading)
21 Midterm week Midterm exam exam guide
23 Midterm week
14 28 MapReduce-3: graphs
(Mandatory Reading) (Optional Reading)
hw 5 due
15 30 SVD-1: basic definition
(Optional Reading)
Progress report due (10:29 am)
May 5 No class (holiday)
7 Project progress presentation.
16 12 SVD-2: case studies
(Mandatory Reading) (Optional Reading)
hw6 due
17 14 SVD-3: properties
(Optional Reading)
18 19 Tensor Analysis
(Mandatory Reading) (Optional Reading)
hw7 due
19 21 Approximation
(Mandatory Reading)
hw8 due
20 26 Graph compression
(Mandatory Reading) (Optional Reading)
hw9 due
21 28 Community detection
(Optional Reading)
22 June 2 Anomaly detection
(Mandatory Reading) (Optional Reading)
hw10 due
23 4 Conclusion: review lecture
(No Reading)
9 No class - prepare your final report
11 Poster session Final report due (10:29 am)
16 Final exam week Final exam
18 Final exam week

Grading

Late policy - for all deliverables:

Project

Textbook

There is no textbook required.

Prerequisite

Undergraduate level statistics and linear algebra.
Last modified Nov. 29, 2013, by U Kang