ITCS/ITIS 6162/8162 (Hybrid)
Data Mining/ Knowledge Discovery in Databases - KDD
Location: Dubois Center 901
Time: Tuesdays, 11:30am-2:15pm (10 minutes break: 12:45pm)
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.
Student Learning Outcomes:
(1) Understanding data mining concepts
(2) Applying various algorithms for data preparation and analysis
(3) Designing data mining applications and evaluating model performance
(4) Students will also learn to apply these skills to solve real-world problems
Course Syllabus
Attendance Policy:
Attendance is mandatory. If you miss n classes, n>1, n-1 points will be subtracted
from your total number of points.
Excuse absence must be accompanied by official documentation that clearly states that
you were physically unable to make the class.
Office Hours (August 24 - December 4)
If you have questions concerning any topic covered in the class, please join us at the office hours scheduled either in Woodward (Main Campus) or on ZOOM every week. No office hours on October 12-13 (Fall Break)
Office hours are posted below:
..................................
GTA
Vennela Reddy Kattula (e-mail: vkattula@charlotte.edu)
- Office Hours in the KDD Lab (Woodward 402)
Wednesday: ......
Friday: ......
- Office Hours on ZOOM
Monday: .......
ZOOM LINK: https://charlotte-edu.zoom.us/j/........
..................................
Zbigniew Ras
Office Hours on Friday, 11:00am-1:00pm
- In Woodward, Room 430C: Sept 4, 18, Oct 2, 23, Nov 6, 20, Dec 4.
- On ZOOM: Aug 28, Sept 11, 25, Oct 9, 30, Nov 13, 21
ZOOM LINK: https://charlotte-edu.zoom.us/j/..............
If nobody shows up by 11:30am, I will leave the zoom meeting.
Week 1 & 2 (August 18 - Center City 901) & (August 25 - asynchronously)
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]).
[1] Data Preprocessing
[2] Classification Trees, PDF
[3]
Classification Trees(Video by L. Powell)
[4] Association Rules, PDF, Video Lecture Part I, Video Lecture Part II
[5]
Association Rules (Video by L. Powell)
[6] LERS,
PDF
[7]
Granular Computing,
PDF, Video Lecture
[8]
Reducts and Discretization,
PDF
[9]
Reducts(Video by L. Powell)
[10] Discretization(Video by L. Powell)
Exercises/ Problems to solve. Solutions
Week 3 & 4 (September 1 - Center City 901) & (September 8 - asynchronously)
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]), mining imbalanced data (see [4]), SVM strategy (see [5]), attribute selection (see [6]). Get familiar with minimum 2 software packages, RSES (see [2]), Orange or WEKA (see [7]).
[1] Sample Problems
[2] Rough Set Exploration System (RSES) , RSES, RS Manual
[3] Mining Incomplete Data PDF, Video Lecture
[4] Mining Imbalanced Data, LINK
[5] Support Vector Machine PDF
[6] Attribute Selection
[7] Bratko's ORANGE , Orange Sample & WEKA
Exercises/ Problems to solve.
Solutions
Week 5 & 6 (September 15 - Center City 901) & (September 22 - asynchronously)
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.
[1] Action Rules and Meta-Actions PDF, Video Lecture
[2] Action Rules Extraction Using Action Reducts
[3] Chase Algorithms PDF, Video Lecture
[4] Sample Problems
[5] Query Answering & New Attributes
Exercises/ Problems to solve.
Solutions
Week 7 & 8 (September 29 - Center City 901) & (October 6 - asynchronously)
O
Learning objectives: Agglomerative and divisive clustering strategies (see [1],[2],[3],[4]). Get familiar with problems and their solutions presented in [5],[6]. If a problem is not entirely solved, complete the solution. Review sample problems presented in [7]. Four of them will be on the midterm exam.
[1] Clustering Methods PDF, Video Lecture
[2] Clustering(Video)
[3] TV Trees PDF
[4] Lance-Williams Algorithm(Video)
[5] Clustering - Sample problems with solutions
[6] Clustering - Sample problems
[7] Sample Problems (Midterm Exam)
Homework: Get familiar with solutions for Midterm Exam sample problems. If problem is not solved, try to solve it.
Week 9 & 10 (October 20 - Center City 901) & (October 27 - asynchronously)
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
[1]Project
[2]Lisp Miner, Video Lecture (by Vamsi Suhas Sadhu), Additional Information
Project Rubric (to be used for grading)
Midterm (in Center City 901): October 20, 11:30-2:00pm
Week 11 & 12 (November 3 - Center City 901) & (November 10 - asynchronously)
Learning objectives: Classifiers evaluation strategies (see [1]), Mining big data (see [2]), mining distributed data (see [3]). Get familiar with problems and their solutions presented in [4]. If a problem is not entirely solved, finish the solution.
[1] Evaluation Methods
[2] Mining Big Data
[3]
Mining Distributed Data
[4]
Sample Problems
Exercises/ Problems to solve.
Week 13 & 14 (November 17 - Center City 901) & (November 24 - asynchronously)
Learning objectives: Review sample problems presented in [2] (Four of them will be on the final exam). KDD Applications to fine art (see [3]) and healthcare (see [4])
[1] Review for the Final Exam
[2] Sample Problems (Final Exam)
[3] Art Analytics (Paintings)
[4] Health Analytics
VIDEO (by Zbyszek Ras)
Week 15 (December 1 - Center City 901)
Learning objectives: Review sample problems presented in [2] (Four of them will be on the final exam). KDD Applications to Business Analytics (see [3])
[1] Review for the Final Exam
[2] Sample Problems (Final Exam)
[3] Business Analytics
FINAL EXAM
December 8 (Tuesday), Center City 901, ........
Project
Upload the project report and the dataset you created to Canvas or email them to
Vennela Reddy Kattula (e-mail: vkattula@charlotte.edu) not later than November 29 (Sunday).
Project Rubric (to be used for grading)
Midterm (in Center City 901): October 20 (Tuesday), 11:30-2:00pm
Final (in Center City 901): December 8 (Tuesday), .......
Points: Midterm - 30 points, Final - 30 points, Project - 40 points
Attendance Policy:
If you miss n classes, where n ≥ 2, n-2 points will be subtracted from your total number of points.
Grades: A [90-100], B [80-89], C [65-79].
Instructor: Zbigniew W. Ras
Office: Woodward Hall 430C
e-mail: ras@charlotte.edu
GTA:
Vennela Reddy Kattula
Office: Woodward Hall 402 (KDD Lab)
e-mail: vkattula@charlotte.edu