CMSC571 Data Mining 

Spring 2025

 

 

Instructor: Dr. Jeonghwa Lee

Office:  MCT 187

Office Hours:  MW 2:30pm - 3pm, 5pm - 6:30pm, T 12pm - 1pm, or by appointment

Phone: 717-477-1019

E-Mail: jlee@ship.edu

Class Website: http://www.cs.ship.edu/~jlee/teaching/spring2025/cmsc571

Class Time & Room:  M 6:30 - 9:15pm, MCT263

 

 

Textbook: 

  Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar, Introduction to Data Mining, 2/E, ISBN: 9780134080284. 

 

Course Objectives:

This course will cover fundamental data mining methodologies and with the ability to formulate and solve problems with them. Several topics will be discussed to introduce the tools and techniques that are used in data mining. Practical, efficient and statistically sound techniques will be discussed. Students will have hands-on projects using Python to develop basic execution skills.  

 

Attendance: 

Attendance in this course is necessary to understand the material and is mandatory. If you must unavoidably miss a class, it is your responsibility to find out what was covered and what was assigned and to get notes from a fellow student. 

 

Homeworks, Projects and Exams: 

Assignments and projects will be announced in the class and also be posted on the class website (http://www.cs.ship.edu/~jlee/teaching/spring2025/cmsc571).
There will be a midterm on March 3, 2025 . There will be one final comprehensive exam based on April 28, 2025 .
No late homeworks and projects will be accepted. 

 


Grading Policy:

Midterm = 20%, Final = 30%, Projects and Homeworks = 35%, Labs and Quizzes = 15%. 

Final grade will be computed as follows:
A : 92 -- 100 A- : 90 -- 91 B+ : 88 -- 89 B : 82 -- 87 B- : 80 -- 81 C : 70 -- 79 F : Below 70 

 

Academic honesty: 

PLAGIARISM and CHEATING are serious academic offenses. The University regulations pertaining to this matter can be found at Shippensburg University Policies on Plagiarism and Other Forms of Academic Dishonesty. Plagiarism and cheating will result in a score of zero on any test, assignment, or program. 

 


Withdrawals: 

To withdraw from a class or from the University, you must notify your academic dean's office. The deadline to withdraw without academic penalty (with grades of "W") is Tuesday, April 1, 2025. If you stop attending class but do not withdraw, you will remain registered and will receive a grade of "F" for the course. You will not receive refunds or adjustments to your account if you do not officially notify the University of your withdrawal. It is important to notify your dean's office in a timely way.

 

Tentative Course Outline: 

  • Overview of Data Mining and Data Processing  
  • Mining Frequent Patterns, Associations, and Correlations  
  • KNN classifier, Bayesian classification, rule-based classification, neural networks, support vector machines  
  • Associative classification,case-based reasoning, genetic algorithms, and decision tree  
  • Data density estimation, anomaly detection, and clustering  
  • Selected Topics  

 

Title IX [https://www.ship.edu/about/offices/hr/title_ix_statement/]