Class Hours: Mon 3:00pm - 4:40pm / Thu 3:00pm - 3:50pm
Lab Hours: Thu 4:00pm - 4:50pm
Classroom: Chang-471

Professor: Dr. Young-Rae Cho   (email: youngcho@yonsei.ac.kr)
       Office: Chang-328
TA: Jong-Hoon Park   (email: jonghoon_park@yonsei.ac.kr)
       Office: Chang-327

Course Web Page: https://ads.yonsei.ac.kr/faculty/data_mining/

Description

Introduction to the concepts, techniques and applications of data mining. Topics include (1) data mining concepts and methods such as association rule mining, pattern mining, classification and clustering, and (2) applications of data mining techniques to complex types of data in various fields.

Objectives

To understand the basic concepts and techniques of Data Mining. To develop computational skills of implementing data mining algorithms to solve practical problems.

Prerequisites

SWE2001, Data Structure Python programming skills

Reference Books

Data Mining: Concepts and Techniques, 3rd Edition, by Jiawei Han, et al., Morgan Kaufmann

Programming Assignments

6 - 9 programming assignments using Python Implementing data mining algorithms during the Lab hours Submission: source codes via LearnUs Late submission: not accepted

Exam

Midterm (Oct. 21): offline exam, closed-book Final Exam (Dec. 16): offline exam, closed-book

Grading

Programming Assignments: 40% Midterm: 20% Final Exam: 25% Attendance & Participation: 15%

Policies

According to the university policy, absences more than 30% will cause getting an F as the final grade of the course no matter what scores are obtained in exames and assignments. (An official excuse letter will be accepted.) Discussions on the programming assignments are allowed, but all programming assignments must be independent work. Any forms of cheating on the assignments and exam will cause a penalty of getting an F as the final grade of the course according to the university regulation guidelines.

Topics & Tentative Schedule

Week Lecture Topics Assignments 1  Introduction   2  Frequent Pattern Mining   3   4  Clustering   5   6   7  Graph Data Mining   8  Midterm   9  Graph Data Mining   10  Sequence Data Mining   11  Classification   12   13  Data Preprocessing   14   15  Review   16  Final Exam