CSE-435 Data Mining

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Room: A-510 Desk No. : 06

Time Day	09:00 - 10:20	10:20 - 11:40	11:40 - 01:20	Break	01:30 - 02:50	02:50 - 04:10
Sat	CSE-435 212_D1 R: J-109	Research Time	CSE-436 212_D2 R: K-101
Sun	CSE-315 221_D10 R: A-605	Tutor Time	CSE-436 213_D1 R: A-501
Mon		Research Time			Weekly Academic Meeting
Tue	CSE-315 221_D10 R: A-605	Tutor Time			CSE 316 221_D20 R: K-109
Wed
	08:30 - 09:50	09:50 - 11:10	11:10 - 12:50	Break	02:00 - 03:20	03:20 - 04:40
Fri	CSE-435 212_D1 R: J-109	Tutor Time	CSE-436 212_D1 R: J-108

Lecture	Selected Topic	Article	Problems
(1-3)	Introduction to Data Mining and its applications
	What Is Data Mining? Major Issues in Data Mining	Ch. 1	Ex. 1.2-1.5, 1.7, 1.9
(4-7)	Knowing data, Structured Data, Statistical Data Descriptions
	Data Visualization, Data Similarity and Dissimilarity	Ch. 2	Ex. 2.2-2.4, 2.6, 2.8
(8-9)	Data Preprocessing, Cleaning, Integration, and Reduction	Ch. 3	Ex. 3.1-3.8
(10)	Mining Frequent Patterns	Ch. 6.1
(11-12)	Mining Frequent Patterns with Apriori Algorithm	Ch. 6.2	Ex. 6.1-6.5
(13-14)	Mining Frequent Patterns with Pattern Growth Approach	Ch. 6.2.4
(15)	Pattern Evaluation Methods	Ch. 6.3
	Midterm
(16-18)	Classification
	Decision Tree
	Bayes Classification	Ch. 8	Ex. 8.6, 8.7, 8.9
(19-21)	Rule-Based Classification, Evaluation, and Improvement	Ch. 8	Ex. 8.13
(22-24)	k-Nearest Neighbor, SVM
	Backpropagation, Neural Network	Ch. 9	Ex. 9.1-9.3
(25-27)	Clustering
	k-Means, k-Medoids, DBSCAN, Hierarchical Clustering	Ch. 10.1-10.4	Ex. 10.2-10.7
(28-29)	Sequential Pattern Mining, GSP Algorithm
(30)	Presentation and Assignment submission	-