CS 521: Data Mining Techniques

Instructor: Abdullah Mueen
Time: Monday 2:00 pm - 4:30 pm
Room: Centennial Engineering Center B146A
Office Hours: Wednesday 1:30-3:00PM and Thursday, 3:30PM-4:00PM
Office: Travelstead Hall, B01A (Knock if the door is closed)
Use the following email address to submit any assignment, ask questions and make comments. Do not send to our personal email address. The email address is correct.
Email Address:  
cs521.unm@gmail.com

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Syllabus

Description: This course covers data mining topics from basic to advanced level. Topics include data cleaning, clustering, classification, outlier detection, association-rule discovery, tools and technologies for data mining and algorithms for mining complex data such as graphs, text and sequences. Students will work on a data mining project to gather hands-on experience.

The course learning objectives include


Book: Data Mining: Concepts and Techniques, 3rd ed.
We will be occasionally referring to this book by Charu Aggarwal. The book is freely available to download in campus network.

Lecture Schedule:
 Here

Grading: There will a final exam worth 35% of the grade. Students will pick datasets for projects and apply mining algorithms. Project is worth 40%. There will be three to five homework, together they are 20% of the course. Homework will focus on understanding the algorithms and techniques. Remaining 5% will be on class participation and attendance.

Academic Calendar: For a list of dates to enroll, change, withdraw classes and a list of hoildays go here.

Project:
Each student will do one project. A project consists of four phases with equal weights.

1.     Classification: Perform classification on the chosen dataset and produce cross-validated precision/recall numbers.

        Due: Oct 3, 2016. Send a report to class email address. Use plots and charts to describe your project. Write the report assuming you would submit it for publication in a journal.

        Requirements:


2.    Clustering: Perform clustering on the chosen dataset and produce meaningful clusters.

        Due: Oct 30, 2016. Send a report to class email address. Use plots and charts to describe your project. Write the report assuming you would submit it for publication in a journal.

        Requirements:



3.    Outlier Detection: Perform outlier detection algorithms on the given dataset and identify anomalous behavior.


        Due: November 20, 2016. Send a report to class email address. Use plots and charts to describe your project. Write the report assuming you would submit it for publication in a journal.

        Requirements:



4.    Ensembling: Perform an ensembling technique to improve accuracy of any of the above tasks.


        Due: December 15, 2016. Send a report to class email address. Use plots and charts to describe your project. Write the report assuming you would submit it for publication in a journal.

        Requirements:

In each phase, a student produces a report describing data cleaning, method(s), results, and discussions. Phase specific goals will be announced in the class page. A student will merge four small reports in a final report and submit in the finals week.

      The datasets are
       
ParkinsonsOddBall Dr. Cavanagh , UNMPraveen Kumar, Dejun Jiang
DiffractionPatterns
Dr. DeGraef , CMUVictor Nevarez
ScrizophreniaEEG      Dr. Calhoun , UNMMaren Pielka
KYMesonet
Dr. Mahmood , WKUConrad Anthony Woidyla, Nicholas Lee Buonaiuto
CIFtoStructureDr. Mueen , UNMAdnan I. Khair
NTvsEQDr. Mueen , UNM
StreusleDr. Schneider, GeorgetownMichael A Regan

Homework:

No late submissions will be accepted. There will be no make-up exams except for university-excused absences. Please discuss unusual circumstances in advance with the instructor.

Homework 1: Click here. Due: Monday 9/26. Submit in the class

Homework 2: Click Here. Due: Monday 10/26, Submit in the class.

Homework 3: Click Here. Due: Monday 11/14, Submit in the class.

Homework 4:



Tools:
Slides:


No form of discrimination, sexual harassment, or sexual misconduct will be tolerated in this class or at UNM in general. I strongly encourage you to report any problems you have in this regard to the appropriate person at UNM. As described below, I must report any such incidents of which I become aware to the university. UNM also has confidential counselors available through UNM Student Health and Counseling (SHAC), UNM Counseling and Referral Services (CARS), and UNM LoboRespect.

UNM faculty, Teaching Assistants, and Graduate Assistants are considered "responsible employees" by the Department of Education (see pg 15 - http://www2.ed.gov/about/offices/list/ocr/docs/qa-201404-title-ix.pdf). This designation requires that any report of gender discrimination which includes sexual harassment, sexual misconduct and sexual violence made to a faculty member, TA, or GA must be reported to the Title IX Coordinator at the Office of Equal Opportunity (oeo.unm.edu).

Complete more information on the UNM policy regarding sexual misconduct, including reporting, counseling, and legal options, is available online: https://policy.unm.edu/university-policies/2000/2740.html