Class Hours: Tue 11:00am - 1:00pm / Thu 11:00am - 12:00pm
Lab Sessions: Thu 12:00pm - 1:00pm
Classroom: Chang-527

Professor: Dr. Young-Rae Cho   (email: youngcho@yonsei.ac.kr)
       Office: Chang-328
TA: Gwang-Hyeon Yun   (email: ghyun0130@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

8 programming assignments using Python Implementing data mining algorithms during the Lab sessions (Assignment 1~7) Submission: source codes via LearnUs Late submission: not accepted

Exam

Midterm Exam (Oct. 21, Tue): offline exam, closed-book Final Exam (Dec. 11, Thu): offline exam, closed-book

Grading

Programming Assignments: 45% Midterm Exam: 20% Final Exam: 25% Attendance: 10%

Policies

If you are absent in a Lab session and cannot submit an assignment, then the assignment due to the first absence has 70% of the average of the other assignment scores. The assignment due to the second absence has 30% of the average of the other assignment scores. From the assignment due to the third absence, 0 points are given. 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.) All programming assignments must be independent work. Any forms of cheating on the assignments and exam, such as copying the responce of any AI tools, 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 Lab 1  Introduction  Python Basics 1 2  Data Preprocessing  Python Basics 2 3  Frequent Pattern Mining  Programming Assignment 1 4  Programming Assignment 2 5  Clustering  Programming Assignment 3 6   7  Programming Assignment 4 8  Midterm Exam   9  Classification   10  Programming Assignment 5 11   12  Sequence Data Mining  Programming Assignment 6 13  Graph Data Mining   14  Programming Assignment 7 15  Review, Final Exam   16  -  Programming Assignment 8 due