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 our office hours scheduled on ZOOM every week.

All ZOOM sessions are listed below:

Aileen Benedict

Office Hours ZOOM Link

https://charlotte-edu.zoom.us/j/5965926700

Wednesday & Thursday: 11:00am - 1:00pm

Nikhita Somanchi

Office Hours ZOOM Link

https://charlotte-edu.zoom.us/j/95591109081

Monday & Wednesday: 2:00-4:00pm

Zbigniew Ras

Office Hours ZOOM Link

https://charlotte-edu.zoom.us/j/93349737999

Tuesday: 3:00-5:00pm

(If no one shows up by 3:30pm, 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 [1]), classifiers evaluation strategies (see [2]), mining distributed data and big data (see [3]). Get familiar with problems and their solutions presented in [4]. If a problem is not entirely solved, finish the solution.

Learning objectives: Applying KDD methods to fine art evaluation (see [1]) and to improve human health (see [2]).

Learning objectives: Review sample problems presented in [1]. Four of them will be on the final exam.

Final Exam Solutions

Project and LISp-Miner

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

Aileen Benedict at [abenedi3@uncc.edu] and to Nikhita Somanchi at [nsomanch@uncc.edu]

not later than May 8 (Monday), 2023

Project Rubric (to be used for grading)

Final (on Canvas): May 5 (Friday), 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

https://charlotte-edu.zoom.us/j/93349737999

Tuesday: 3:00-5:00pm

(If nobody shows up by 3:30pm, I will leave the zoom meeting)

Office: Woodward Hall 402 (KDD Lab)

e-mail: abenedi3@uncc.edu

Office: Woodward Hall 402 (KDD Lab)

e-mail: nsomanch@uncc.edu

When: Monday, Wednesday: 2:00-4:00pm

Additional Documents