CSCI 4380/6380 Data Mining

CSCI 4380/6380 Data Mining

Fall 2017: Mondays 3:35pm - 4:25pm & Tuesdays and Thursdays 3:30pm - 4:45pm, Boyd GSRC Room 208

Instructor: Prof. Khaled Rasheed
Telephone: (706)542-0881
Office Hours: Monday 4:30-6:00pm and Thursday 2:00-3:00pm or by email appointment
Office Location: Room 111, Boyd GSRC

Teaching Assistant: Roxana Attar
Office Hours: Wednesday 1:30pm-4:00pm
Office Location: Room 537, Boyd GSRC


The course aims to provide students with a broad introduction to the field of Data Mining and related areas and to teach students how to apply these methods to solve problems in complex domains. The course is appropriate both for students preparing for research in Data Mining and Machine Learning, as well as Bioinformatics, Science and Engineering students who want to apply Data Mining techniques to solve problems in their fields of study.

Recommended Background:

CSCI 2720 Data Structures. Familiarity with basic computer algorithms and data structures. Familiarity with the Java programming language is recommended but not required.

Topics to be Covered:

  • Part I: Data Mining techniques: Selected from: Association and Classification Rule Mining, Linear Models, Decision Trees and Random Forests, Neural Network approaches, Support Vector Machines, Bayesian Learning, Instance-based Learning, Pre-processing and Feature Selection, Performance evaluation, Ensemble Learning and clustering.
  • Part II: Data Mining applications: Selected from: Bioinformatics, Biomedical/Physical/Chemical modeling, medical diagnosis, text/web mining, pattern recognition and/or other contemporary applications.

    Expected Work:

    Reading; assignments (include running experiments using the Weka package); paper presentation, two midterms; and term project (may require programming or running existing packages) and paper. Unless otherwise announced by the instructor, all assignments and all exams must be done entirely on your own.

    Academic Honesty and Integrity:

    All academic work must meet the standards contained in "A Culture of Honesty." Students are responsible for informing themselves about those standards before performing any academic work. The penalties for academic dishonesty are severe and ignorance is not an acceptable defense.

    Grading Policy:

  • Assignments: 20% (Programs, homeworks, attendance, paper presentation)
  • Midterm Examinations: 40%
  • Term Project: 40% (includes term paper and presentation)
    Students may work on their term projects in groups of up to FOUR students each. The above distribution is only tentative and may change later. The instructor will announce any changes.

    Assignment Submission Policy

    Assignments must be turned in by the assigned deadline. Late assignments will not be accepted. Rare exceptions may be made by the instructor only under extenuating circumstances and in accordance with the university policies.

    Course Home-page

    A variety of materials will be made available on the DM Class Home-page at, including handouts, lecture notes and assignments. Announcements may be posted between class meetings. You are responsible for being aware of whatever information is posted there.

    Lecture Notes

    Copies of some of Dr. Rasheed's lecture notes will be available at the bottom of the class home page. Not all the lectures will have electronic notes though and the students should be prepared to take notes inside the lecture at any time.

    Textbook in Bookstore

  • "Data Mining: Practical Machine Learning Tools and Techniques (4th edition)", Ian Witten, Eibe Frank , Mark Hall and Christopher Pal. Morgan Kaufmann, 2016. (Required)
    ISBN-10: 0128042915 & ISBN-13: 978-0128042915

    Web Resources

  • The WEKA Machine Learning Project
  • University of California at Irvine ML Repository
  • David Aha's Machine Learning Resources


  • [12-4-2017] The course evaluations for CSCI courses are to be done on-line for Fall 2017. You can access the following URL which will allow you to login into the course evaluation system using your MyID and password: The course evaluation system will be on-line till December 7, 12:00AM (finals begin on December 7). Please remember that you will get 1\% extra credit for your total class grade just for doing the evaluations!
  • [12-4-2017] The course project presentations will be done on 12-12-2017 at the time of the final exam (because there is no final exam). They will be done from 3:30 pm to 6:30 pm with about 15 minutes per group in Boyd room 208. The course project reports are due on the same day. For the project report format, please write it as a conference paper of about 8 two-column pages or 12 single-column pages (there is no restriction on size though). You should include a title, an abstract, an introduction, a mention of related work if any, a description of your experiments and results and a conclusion. In the introduction or elsewhere in the paper you should describe the domain that you applied your Data Mining technique(s) to, in enough detail for the reader to appreciate the significance and difficulty of the problem. Please bring a hard copy. Please also include your email addresses as well as the URLs of any demo/supporting web pages. There is a slight chance that I might contact you soon after the submission deadline (within 48 hours) requesting codes, clarifications or more data.
  • [11-28-2017] The second midterm will be this Thursday 11-30-2017. It will focus on Chapter 5 and the sections of Chapters 7 and 12 that are covered in the lecture notes but please bring all lecture notes for all chapters with you. It will be open notes but the use of laptops or phones will not be allowed. You should also bring your lecture notes and all handouts and you may also bring any additional notes, homeworks etc.
  • [11-22-2017] To improve the value of your course project there are many things you can do. I list some of them in this announcement. Feel free to contact me regarding your specific project. A good project will typically exercise many of the ideas discussed in the class. You should apply several data mining methods. For classification problems, you can try tree based, SVM based, Neural networks, Bayesian, Nearest neighbor, logistic regression or other suitable methods. For clustering problems you can try several clustering approaches including Hierarchical, Kmeans, spectral, EM or other suitable methods. You should try to improve performance using ideas such as hyper parameter tuning, discretization, preprocessing or other suitable ideas. You should analyze the performance of the different methods using appropriate performance measures. You should also try to combine multiple classifiers using bagging, boosting and/or stacking if applicable. In summary, I recommend you start with one method and close the loop, then add the other methods, do the tuning, analyse performance and finish with ensemble learning.
  • [10-10-2017] The first midterm exam will be this Thursday 10-12-2017. It will cover all the topics discussed in the course till last Thursday (i.e. up to Chapter 5 Page 166). It will be open notes but the use of books, laptops or phones will not be allowed. You should bring a calculator to the exam; If you do not have a calculator you may use your phone as a calculator. You should also bring your lecture notes and all handouts and you may also bring any additional notes, homeworks etc.


  • "Unsupervised feature selection for multi-cluster data" 2010. [Qinglin Dong][11/06] {download}
  • "Clustering by Passing Messages Between Data Points" 2007. [Joshwa Shannon][11/07] {download}
  • "Extending market basket analysis with graph mining techniques: A real case" 2014. [Zach Baker][11/07] {download}
  • "Text and Structural Data Mining of Influenza Mentions in Web and Social Media" 2010. [Amy Giuntini][11/09] {download}
  • "ImageNet classification with deep convolutional neural networks" 2017. [Zach Jones][11/09] {download}
  • "Authorship Verification for Short Messages using Stylometry" 2013. [Isela Diaz Martinez][11/09] {download}
  • "Feature Mining for Image Classification" 2014. [Hari Teja Tatavarti][11/13] {download}
  • "Emotional state classification from EEG data using machine learning approach" 2014. [Shulin Zhang][11/14] {download}
  • "Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction" 2011. [Christian McDaniel][11/16] {download}
  • "Deep Bilateral Learning for Real-Time Image Enhancement" 2017. [Mahdi Kashanipour][11/27] {download}
  • "Why didn’t my (great!) protocol get adopted?" 2015. [I-Huei Ho][11/27] {download}
  • "Least squares support vector machines ensemble models for credit scoring" 2010. [Nicholas Klepp][11/28] {download}
  • "An Efficient Approach for Image Recognition using Data Mining" 2011. [Kang Yuan][12/4] {download}
  • "Neural Turing Machines" 2014. [Layton Hayes][12/4] {download}


  • Homework 1: Exercise 17.1 on pages 559 - 565 of the Weka exercises handout given in class today. You can also download all the exercises from HERE. [Due 9-7-2017 in class]
  • Homework 2
  • Homework 3
  • Homework 4: Exercise 17.4 on page 574 of the Weka ecercises handout.[Due 10-31-2017 in class]
  • Homework 5
  • Homework 6

    Lecture Notes:

  • Chapter 1
  • Chapter 2
  • Chapter 3
  • Weka Tutorial Slides by Roxana Attar
  • Chapter 4
  • Chapter 5
  • Chapter 7
  • Chapter 12
    The course syllabus is a general plan for the course; deviations announced to the class by the instructor may be necessary.

    Last modified: December 4, 2017.

    Khaled Rasheed (khaled[at]