ITCS 3162 (Introduction to Data Mining)

ITCS 3162 Introduction to Data Mining
Fall 2021

Syllabus::

Data preprocessing
Association and classification rules
Decision trees (tree classifiers)
Granular computing and Data Semantics
Rough Sets based classifiers (RSES, LERS)
Data discretization
Data reduction (preserving knowledge hidden in data)
Data mining methods: SVM, Random Forest, NN Classifiers,...
Mining incomplete data
Data actionability (action rules and meta-actions)
Chase algorithms
Evaluation methods (confusion matrix, ...)
Cluster analysis
Query processing and mining big data

Google Drive Class Folder

What subjects and when will be covered:

	Date	Topic	Reading	Slides

Week 1 (Ras)	January 20	Data preprocessing Classification Trees Classification Rules (LERS)	Lecture 1 Lecture 2 Lecture 3 Sample Problems	PowerPoint, PDF PowerPoint, PDF Video PowerPoint, PDF Document
Week 2 (Ras)	January 27	Association Rules Representative Rules	Lecture 4 Sample Problems	PowerPoint, PDF Video Document
Week 3 (Benedict)	February 3	Orange Weka	DM Systems/Examples	Link Link
Week 4 (Ras)	February 17	Granular Computing Reducts Discretization RSES Software	Lecture 5 Sample Problems Software Orange Display	PowerPoint, PDF PowerPoint, PDF Document Video Video
Week 5 (Ras)	February 24	SVM Random Forest Mining Incomplete Data Evaluation Methods	Lecture 6 Lecture 7 Sample Problems Lecture 8	PowerPoint PowerPoint Document PDF
Week 6 (Benedict)	March 3	Sample Problems Exercises with classifiers: Orange, Weka, RSES.		Document
Week 7 (Ras)	March 10	Review		Document
Week 8 (Benedict)	March 17	Exam I		PDF
Week 9 (Ras)	March 24	Solutions to Exam I Action Rules	Lecture 9 Sample Problems	PowerPoint Document
Week 10 (Ras)	March 31	Clustering I Project	Clustering Methods Sample Problems	PowerPoint Video Document
Week 11 (Benedict)	April 7	LISP Miner LISP Miner Instalation LISP Miner Manual Comparing Classifiers (in Weka or Orange)	Lecture 11	PowerPoint Good to know Manual
Week 12 (Ras)	April 14	Review		Document
Week 13 (Benedict)	April 21	Exam II		PDF
Week 14 (Ras)	April 28	Chase Algorithms Examples		Link Document
Week 15 (Ras)	May 5	Review		Link
	May 6	PROJECT DUE DATE Maximum 4 people on the team Project Rubric
Final (Benedict)	May 12	Final Exam	........

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

Project - deadline to submit: May 3, 2021

Final: May 12, 2021 (Wednesday), (Canvas/Webex)

Grades

Exams - 20 points each, Final - 30 points, Project - 30 points (maximum)
Grade A from 90 to 100 points, Grade B from 79 to 89 points, Grade C from 63 to 78, Grade D from 47 to 62 points.

Sample Problems

Class meetings:

Location: Webex (Invitations will be sent every Tuesday evening)
Time: Wednesday, 11:30am-14:15pm

Instructor: Zbigniew W. Ras

Office: Location: Woodward Hall 430C
Telephone: 704-687-8574
Office Hours - Time: Wednesday (10:00-11:00am)
Office Hours - Location: On Webex (https://uncc.webex.com/meet/ras)
e-mail: ras@uncc.edu

GTA and co-Instructor: Aileen Benedict

Office: Location: https://uncc.webex.com/meet/abenedi3
Telephone: 704-687-8546
Office Hours on WebEx: Monday, Friday (11:00am-12:00pm)
e-mail: abenedi3@uncc.edu

GTA: Yuehua Duan

Office: Location: https://uncc.webex.com/meet/yduan2
Telephone: 704-687-8546
Office Hours on WebEx: Friday, 1:30-5:30pm
e-mail: yduan2@uncc.edu