Prerequisites: ITCS6160, full graduate standing or content of the department.

Textbook (not required): "Introduction to Data Mining", by Pang-Ning Tan, Michael Steinbauch,
Vipin Kumar, Addison Wesley.

please join me or my TAs during office hours scheduled on ZOOM every week.

All ZOOM sessions are listed below:

Yuehua Duan at https://uncc.zoom.us/j/91258655476

Monday, Wednesday, Friday: 9:00-11:00am

Rishab Semlani at https://uncc.zoom.us/j/95898300120

Tuesday, Wednesday, Thursday: 3:00-5:00pm

Zbigniew Ras at https://uncc.zoom.us/j/92716093575

Tuesday & Thursday (till February 18), 11:30am-1:00pm

February 22, 12:00-1:00pm; February 24 & March 1, 12:30-2:00pm

(If nobody shows up during the first 30 minutes, I will leave the zoom meeting)

Learning objectives: Classification tree construction using entropy and Gini Index (see [2],[3]), association and representative rules discovery (see [4],[5]), classification rules discovery usung LERS (see [6]), computing reducts (using discernibility matrix or heuristic strategy based on attribute selection technique), data discretization, classification rules construction using discernibility functions for dataset objects (see [7],[8],[9],[10]).

Learning objectives: Get familiar with problems and their solutions presented in [1]. If a problem is not entirely solved, complete the solution. Rules discovery from incomplete datasets using tolerance relation (see [3]) and SVM strategy (see [4]). Get familiar with minimum 2 software packages, RSES (see [2]), Orange or WEKA (see [5]).

Learning objectives: Action rules construction methods DEAR 1, DEAR 2 (see [1]) and strategy based on action reducts (see [2]). Strategy Chase for revealing hidden values in datasets [3]. Get familiar with problems and their solutions presented in [4],[5]. If a problem is not entirely solved, complete the solution.

Learning objectives: Agglomerative and divisive clustering strategies (see [1],[2],[3]). Get familiar with problems and their solutions presented in [4],[5]. If a problem is not entirely solved, complete the solution. Review sample problems presented in [6]. Four of them will be on the midterm exam.

Learning objectives: Class group project assignment (see [1]) and software package for action rules discovery called LispMiner (see [2]) which you need to learn to complete the project. MIDTERM EXAM

Learning objectives: Data sanitization method against chase (see [2]), classifiers evaluation strategies (see [3]), mining distributed data and big data (see [4]). Get familiar with problems and their solutions presented in [5]. If a problem is not entirely solved, finish the solution.

Learning objectives: Applying KDD methods to fine art evaluation (see [2]) and improving human health (see [1]). Review sample problems presented in [3]. Four of them will be on the final exam.

Project and LISp-Miner

Upload the project report and the dataset you created to Canvas or email them to

Rishab Semlani at [rsemlani@uncc.edu] and Yuehua Duan at [yduan2@uncc.edu]

not later than February 27 (Sunday), 2022

Project Rubric (to be used for grading)

Final (on Canvas): March 1 (Tuesday), 4:00-6:30pm

Points: Midterm - 30 points, Final - 30 points, Project - 40 points

Grades: A [90-100], B [80-89], C [65-79].

Office: Woodward Hall 430C

Telephone: 704-687-8574

e-mail: ras@uncc.edu

Tuesday & Thursday, 11:30am-1:00pm

(If nobody shows up by noon, I will leave the zoom meeting)

Office: Woodward Hall 402 (KDD Lab)

e-mail: yduan2@uncc.edu

Monday, Wednesday, Friday: 9:00-11:00am

Office: Woodward Hall 402 (KDD Lab)

e-mail: rsemlani@uncc.edu

Tuesday, Wednesday, Thursday: 3:00-5:00pm

Additional Documents