CSC240/440: Data Mining
Spring 2019

General Information

Instructor: Prof. Ted Pawlicki <pawlicki@cs.rochester.edu>

Office: 2101 Wegmans Hall (see instructor website for office hours)

Lectures: Tuesday & Thursday 1650-1805 in Gavett 202

Web: BlackBoard

Questions? Head TA & Workshop super-leader: csc240@cs.rochester.edu

Course Description

Fundamental concepts and techniques of data mining including data attributes, data visualization, data pre-processing, mining frequent patterns, association and correlation, classification methods, and cluster analysis. Advanced topics (time permitting) include outlier detection, stream mining, and social media data mining.

Prerequisites

MTH161, CSC171, CSC172 . Some knowledge of probability theory (CSC262) and artificial intelligence (CSC242) will be helpful.

Textbook

Data Mining : Concepts and Techniques 3/E Han, Micheline, Kamper and Pei

Overview and Introduction(Chapter 1)
Getting to Know Your Data(Chapter 2)
Data Preprocessing(Chapter 3)
Mining Frequent Patterns(Chapter 6)
Association and Correlation(Chapter 6)
Classification : Basic Concepts(Chapter 8)
Cluster Analysis : Basic Concepts(Chapter 10)
Advanced Pattern Mining(Chapter 7)*
Outlier Detection(Chapter 12)
Classification : Advanced Concepts(Chapter 9)*
Cluster Analysis : Advanced Methods(Chapter 11)
Invited Guest LecturesTo be confirmed
Future Trends and Research Frontiers(Chapter 13)
* Advanced Topics

Course Work

Midterm Exam : March 7th

Five Homework assignments based on textbook readings and lectures.

Final Project Proposal : Late March.

Final Project Presentations : Late April (last 4 class meetings)

Grading

Late homework or projects without suitable prearranged excuse will not be accepted. Missed or late homework will receive a grade of zero.

Final course grades will be based on the following components and weights:

Midterm Exam30%
Homework Assignments (5)25%
Project 110%
Project 2 : Written Report25%
Project 2 : Presentation 10%

Homework and Projects are due at 11:55pm on the due date. All submission on Blackboard online. Only the latest submission is considered.

Graduate Students will have additional problems in homework and project 1.

Programming will be required for the small project and the final project. Use of online resources is allowed as long as proper attribution is given.

Regrading deadline is one week from the date grades are posted.

Final Project

The goal of the project is to develop a data mining solution to predict an outcome, discover patterns or classify events.

Team-based activity (maximum team size is 2). Each team will choose from one of the suggested data sets.

Expectation:

Letter grades will follow the Official University of Rochester Grading Scheme. Note that the University scheme puts average somewhere between C and B. The following table is an estimate of how the numeric grades will map onto the letter grades (subject to change):

A: Excellent >=90%
B: Above Average >=80%
C: Minimum Satisfactory Grade >=70%
D: Minimum Passing Grade >=60%
E: Fail <60%

All appeals of grades for any component of the course (homework, project, quiz, exam, etc.) must be made within ONE WEEK of the grade being posted.

Course Policies

We hope that you will want to attend class (lecture), but attendance is NOT required other than for exams. If you chose not to attend, you may miss important announcements or information about the course.

If you use your own computer, crashes, malfunctions, and catastrophic loss of files is NOT an excuse. Backup your files regularly to at least one external drive and/or cloud storage. You can always complete your assignments using the lab and IT Center computers.

Students with an appropriate excuse for missing a quiz, workshop, homework, or project deadline must make arrangements in advance.

Students with an accommodation for any aspect of the course must make arrangements through the Center for Excellence in Teaching and Learning (CETL) in advance. Then, as instructed by CETL, contact the instructor to confirm your arrangements. Do not leave this until the last minute either.

Students who are unable to attend or complete any part of the course due to illness should contact the instructor AS SOON AS POSSIBLE. Please note that the University Health Service (UHS) does not provide retroactive excuses for missed classes. Students who are seen at UHS for an illness or injury can ask for documentation that verifies the date of their visit(s) to UHS without mention of the reason for the visit. Students with extended or severe illness should contact the College Center for Advising Services (CCAS) for advice and assistance.

Academic Honesty and Collaboration

All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy. More information is available at: www.rochester.edu/college/honesty

You will learn the most if you do all the work in this course ON YOUR OWN.

Homework is individual work and you must complete it individually. Your TAs and are available in lab to help you with homework. You may verbally discuss assignments with tutors and other students, but you must write all assignments individually.

Collaboration on projects is permitted, subject to the following requirements:

Policy on Electronics

You do not need any electronics in class and they will not help you or your fellow students seated near you.

Unless you are taking notes with a laptop, you do not need it.

Even if you think you want to take notes with a laptop, you may be interested to know that research shows that students who take notes using pen and paper retain significantly more of the information. Typing your handwritten notes into the computer after class improves understanding even more.

Please note Section V.7 of the College's Academic Honesty policy regarding Unauthorized Recording, Distribution or Publication of Course-Related Materials.

CETL Tutoring

Tutoring is a free service available for any undergraduate of the University of Rochester. The Center for Excellence in Teaching and Learning (CETL) provides structured tutoring services.

CETL Tutoring : www.rochester.edu/college/cetl/undergraduate/tutoring.html

CSUG Tutoring

CSUG is the Computer Science Undergraduate Council. These students graciously volunteer their time to help other students, especially students in introductory and core courses.

CSUG Tutoring Schedule: www.csug.rochester.edu/ugc/tutoring/

If you visit the CSUG tutors, please be respectful of their time and COME PREPARED. Try to solve the problem/question BEFORE going to CSUG Tutoring. Go to lab and work on the problem with the TAs BEFORE going to CSUG Tutoring. Go to workshop and discuss the problem with your colleagues BEFORE going to CSUG Tutoring. You MUST be able to show them what you have tried BEFORE you came to them.