DSBA/ITCS 6162/8162

Knowledge Discovery in Databases - KDD

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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.

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Course Syllabus

Lectures (Class Videos available on LINK)

August 24

[1] Data Preprocessing

[2] Classification Trees, PDF

[3] Classification Trees(Video by L. Powell)

[4] Association Rules, PDF

[5] Association Rules (Video by L. Powell)

August 31

[1] LERS, PDF

[2] Granular Computing, PDF

[3] Reducts and Discretization, PDF

[4] Reducts(Video by L. Powell)

[5] Discretization(Video by L. Powell)

Sept 7

[1] Sample Problems

[2] Rough Set Exploration System (RSES)

September 14

[1] Mining Incomplete Data PDF

[2] Support Vector Machine PDF

[3] Bratko's ORANGE or WEKA

[4] Sample Problems

September 21

[1] Action Rules and Meta-Actions PDF

[2] Action Rules Extraction Using Action Reducts

September 28

[1] Chase Algorithms PDF

[2] Data Sanitization PDF

[3] Exercise

October 5

[1] Clustering Methods PDF

[2] Clustering(Video)

[3] TV Trees PDF

[4] Clustering - Sample problems with solutions

October 19

[1] Clustering - Sample problems

[2] Sample Problems (Midterm Exam), Part I

October 26

1:00am-2:30pm (Lecture by Zbyszek Ras)
[1] Sample Problems (Midterm Exam), Part II & Project

2:45-3:45pm (Lecture by Sapna Pareek)
[2] Lisp Miner

November 2

Midterm Exam (on CANVAS)

November 9

[1] Solutions to Midterm Exam

[2] Project Questions & Answering Session

[3] Data Sanitization PDF Example

November 16

[1] Evaluation Methods

[2] Distributed Data and Big Data

[3] Sample Problems

November 23 (NO CLASS: Asynchronous online learning)
KDD Application Areas
(PPT presentations & VIDEOS you need to get familiar with)

[1] Health Analytics    Procedure Graph

      VIDEO

[2] Art Analytics (Paintings)

      VIDEO (by L. Powell)

November 30
KDD Application Areas

[3] Business Analytics

[4] Art Analytics (Music)

December 7

[2] Sample Problems (Final Exam)

December 14

Final Exam (on CANVAS); 11:00am - 1:30pm

Project
Project and LISp-Miner
Upload the project report and the dataset you created to Canvas or send them to
Sapna Pareek at [spareek@uncc.edu]
not later than December 6 (Monday), 2021
Project Rubric

Midterm (on Canvas): November 2
Final (on Canvas): December 14, 11:00am - 1:30pm
Points: Midterm - 30 points, Final - 30 points, Project - 40 points

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

Instructor:       Zbigniew W. Ras

Office: Woodward Hall 430C
Telephone: 704-687-8574
e-mail: ras@uncc.edu Office Hours on ZOOM: https://uncc.zoom.us/j/98763508031
Tuesday: 11:30am-12:45pm

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GTA I:       Sapna Pareek

Office: Woodward Hall 402 (KDD Lab)
e-mail: spareek@uncc.edu Office Hours on ZOOM: https://uncc.zoom.us/j/3315691724
Monday: 5:00-6:30pm, Thursday: 2:30-4:00pm

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GTA II:       Rishab Semlani

Office: Woodward Hall 402 (KDD Lab)
e-mail: rsemlani@uncc.edu Office Hours on ZOOM: https://uncc.zoom.us/j/92404241568?pwd=cjZ5MmE2YTlveGYvQUg4dGN4R1B2QT09
Wednesday: 11:00am-12:30pm, Thursday: 11:00am-12:30pm

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[1] Lisp Miner(by Jan Rauch)

[2] Lisp Miner Manual(by Jan Rauch's Student)

[3] Rough Sets

[4] Software for data mining

[5] Repository of large datasets

[6] LERS vs ERID

[7] Extracting Rules from Incomplete Table

[8] Lance & Williams Distance

[9] Sample Problems for Midterm Exam