Seoul National University
M2177.003000 Advanced Data Mining
Fall 2019 - U Kang

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Course Information

Data mining attracted much interests as an essential tool for big data analysis. Especially, designing and implementing advanced data mining algorithms and analysis platforms play crucial roles in extracting executable knowledges from big data. This course covers advanced data mining techniques, algorithms, and core platforms for big data analysis. This course also covers the techniques to effectively analyze very large data and high-speed data.

A very important aspect of this course is the course project. Students will pick an interesting data mining project, and do data mining researches on the topic. At the end of the course, students will learn how to do interesting researches in data mining, and how to write good papers.


Date Topic Due
Sep. 2 Course Introduction
4 Graph-1: basics and diameter
(Optional Reading)
9 Graph-2: models for graphs
(Mandatory Reading) (Optional Reading)
11 Graph-3: power law
(Mandatory Reading) (Optional Reading)
16 Graph-4: structure analysis
(Optional Reading)
18 Spectral analysis-1: random walk
(Optional Reading)
23 Spectral analysis-2: link analysis
(Mandatory Reading) (Optional Reading)
25 Spectral analysis-3: link prediction
(Optional Reading)
30 Spectral Analysis-4: triangle counting
(Mandatory Reading) (Optional Reading)
Oct. 2 Project proposal presentation 1. Proposal due (15:30 pm)
7 Project proposal presentation 2.
9 MapReduce-1: architecture
(Mandatory Reading)
Hw5 (due: Oct. 11 23:59 pm)
14 MapReduce-2: data mining algorithms
(Optional Reading)
16 MapReduce-2: data mining algorithms (cont.)
21 Midterm week
23 Midterm week
28 SVD-1: basic definition
(Optional Reading)
30 SVD-2: case studies
(Mandatory Reading) (Optional Reading)
Nov 4 Project progress presentation 1.
6 Project progress presentation 2.
11 SVD-3: properties
(Optional Reading)
13 Tensor Analysis
(Mandatory Reading) (Optional Reading)
18 Approximation
(Optional Reading)
20 Graph compression
(Mandatory Reading) (Optional Reading)
25 Community detection
(Optional Reading)
27 Anomaly detection
(Mandatory Reading) (Optional Reading)
Dec. 2 Project final presentation 1.
4 Project final presentation 2.
9 Final exam week
11 Final exam week


Late policy - for all deliverables:



There is no textbook required.


Undergraduate level algorithm, statistics and linear algebra.
Last modified Mar. 16, 2019, by U Kang