PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
CENG 468	DATA MINING	3 + 0	8th Semester	5

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Elective
Course Objective	The aim of the course is give information about Data Mining Concepts, Preparing the Data, Statistical Classification Method (Naïve Bayes), Clustering Methods(K-Means, Hierarchical), Decision Trees and Decision Rules, Association Rules.
Course Content	Introduction to Data Mining / Data Mining Concepts / Preparing the Data / Data Reduction / Statistical Classification Method (Naïve Bayes) / Clustering Methods (K-Means) / Clustering Methods (Hierarchical) Decision Trees and Decision Rules Association Rules / Artificial Neural Networks
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	application ability about the data mining methods

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11	PO 12
LO 001	3	3	3	3	3	3	3	3	3	3	3	3
Sub Total	3	3	3	3	3	3	3	3	3	3	3	3
Contribution	3	3	3	3	3	3	3	3	3	3	3	3

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	2	28
Mid-terms	1	30	30
Final examination	1	30	30
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2023-2024 Fall	1	SERDAR İPLİKÇİ

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
CENG 468	DATA MINING	3 + 0	1	Turkish	2023-2024 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. SERDAR İPLİKÇİ	iplikci@pau.edu.tr		MUH A0435	%70

Goals		The aim of the course is give information about Data Mining Concepts, Preparing the Data, Statistical Classification Method (Naïve Bayes), Clustering Methods(K-Means, Hierarchical), Decision Trees and Decision Rules, Association Rules.
Content		Introduction to Data Mining / Data Mining Concepts / Preparing the Data / Data Reduction / Statistical Classification Method (Naïve Bayes) / Clustering Methods (K-Means) / Clustering Methods (Hierarchical) Decision Trees and Decision Rules Association Rules / Artificial Neural Networks

Topics

Weeks	Topics
1	INTRODUCTION to DATA MINING
2	DATA MINING APPROACHES and APPLICATIONS
3	FREQUENT ITEMSET MINING - Introduction to Frequent Itemset Mining
4	FREQUENT ITEMSET MINING - Apriori Algorithm
5	FREQUENT ITEMSET MINING - Apriori Algorithm
6	FREQUENT ITEMSET MINING - ECLAT Algorithm
7	FREQUENT ITEMSET MINING - ECLAT Algorithm
8	FREQUENT ITEMSET MINING - H-mine Algorithm
9	FREQUENT ITEMSET MINING - H-mine Algorithm
10	FREQUENT ITEMSET MINING - FPtree Algorithm
11	FREQUENT ITEMSET MINING - FPtree Algorithm
12	ASSOCIATION RULE MINING - Association Rules
13	ASSOCIATION RULE MINING - Confidence
14	ASSOCIATION RULE MINING - Interest

Materials

Materials are not specified.

Resources

Resources	Resources Language
Data Mining Concepts and Techniques	English

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student