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CSE 5243-0020 - Autumn 2019

Introduction to Data Mining

Time/Place: 12:45 PM-02:05 PM, Wed/Fri Caldwell Lab 177 Instructor: Dr. Ping Zhang; 310A LT; zhang.10631@osu.edu; http://pingzhang.net Teaching Assistant (TA): Fang Zhou; 574 DL; zhou.1250@osu.edu

Level and credits: U/G, 3

Office Hours: (Instructor) Wed 10:30 AM - 11:30 AM; (TA) Mon 3:30 PM - 4:40 PM Course Description: Knowledge discovery, data mining, data preprocessing, data transformations; clustering, classification, frequent pattern mining, anomaly detection, avoiding false discoveries, graph and network analysis; applications. Prerequisites: Introduction to Databases, Introduction to Algorithms, or grad standing or permission of instructor

Textbooks:

• (Required) Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and

Techniques (3rd ed), 2011

• (Required) Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to data

Mining (1st or 2nd ed), 2006

• (Optional) Charu C. Aggarwal, Data Mining: The Textbook, Springer, 2015. • (Optional) Mohammed J. Zaki and Wagner Meira Jr., Data Mining and Analysis:

Fundamental Concepts and Algorithms, 2014.

Grading Plan:

• Quizzes and participation: 20% o 12 Quizzes o 8 attendances • Homework: 30% o 3 programming assignments • Midterm exam: 25% (Oct 9 in class) • Final exam: 25% (Dec 9 at 4pm) Academic Integrity Policy: Academic integrity is essential to maintaining an environment that fosters excellence in teaching, research, and other educational and scholarly activities. Thus, The Ohio State University and the Committee on Academic Misconduct (COAM) expect that all students have read and understand the University's Code of Student Conduct, and that all students will complete all academic and scholarly assignments with fairness and honesty. Students must recognize that failure to follow the rules and guidelines established in the University's Code of Student Conduct and this syllabus may constitute Academic Misconduct.

Course Syllabus:

Week Date Topic Assignment

1 08/21 Introduction

1 08/23 Introduction

2 08/28 Data & Data Preprocessing

2 08/30 Data & Data Preprocessing

3 09/04 Classification: Basic Concepts/Methods

3 09/06 Classification: Basic Concepts/Methods

4 09/11 Classification: Basic Concepts/Methods

4 09/13 Classification: Basic Concepts/Methods

5 09/18 Classification: Advanced Methods

5 09/20 Classification: Advanced Methods

6 09/25 Association Analysis

6 09/27 Association Analysis

7 10/02 Association Analysis

7 10/04 Association Analysis Assignment 1 Due

8 10/09 Midterm Exam

8 10/11 Autumn Break

9 10/16 Association Analysis: Advanced Concepts

9 10/18 Association Analysis: Advanced Concepts

10 10/23 Association Analysis: Advanced Concepts

10 10/25 Association Analysis: Advanced Concepts

11 10/30 Cluster Analysis: Basic Concepts and Algorithms

11 11/01 Cluster Analysis: Basic Concepts and Algorithms Assignment 2 Due

12 11/06 Cluster Analysis: Basic Concepts and Algorithms

12 11/08 Cluster Analysis: Basic Concepts and Algorithms

13 11/13 Cluster Analysis: Additional Issues and Algorithms

13 11/15 Cluster Analysis: Additional Issues and Algorithms

14 11/20 Anomaly Detection

14 11/22 Avoiding False Discoveries

15 11/27 Thanksgiving Break

15 11/29 Thanksgiving Break Competition Due

16 12/04 Data Competition and AI in Medicine Assignment 3 Due

16 12/06 Exam Week Begin

17 12/09 Final Exam

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