Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP5101 Data Mining Fall 3 0 3 8
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi TEVFİK AYTEKİN
Course Lecturer(s): Dr. Öğr. Üyesi TEVFİK AYTEKİN
Recommended Optional Program Components: None
Course Objectives: This course provides an introduction to data mining concepts. Basic concepts in data mining: frequent item set detection, association rules, clustering and classification are covered in depth

Learning Outcomes

The students who have succeeded in this course;
I. Be able to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process or Data Mining, including the business understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase.
II. Be proficient with leading data mining software, including WEKA
III. Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, classification and regression trees, the C4.5 algorithm, logistic Regression, k-nearest neighbor
IV. Understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues.
V. Understand the mathematical statistics foundations of the algorithms outlined above
VI. Evaluate current research and advanced topics in data mining.

Course Content

1. Frequent Item Set Detection
2. Association Rule Mining
3. Clustering
4. Classification

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to Data Mining None
2) Frequent Item Set Mining None
3) Various frequent item set algorithms: Apriori, FPGrowth None
4) Association Rule Mining None
5) Classification None
6) Bayesian classification None
7) Midterm Exam 1 Review all the topics
8) Rule based classification None
9) Cluster Analysis None
10) k-means None
11) k-medoids None
12) Hierarchical clustering None
13) Cluster quality None
14) Combining Multiple Clusterings None


Course Notes / Textbooks: Data Mining Concepts and Techniques
Jiawei Han and Micheline Kamber
Morgan Kaufman

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Project 5 % 10
Midterms 1 % 40
Final 1 % 50
Total % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Study Hours Out of Class 14 56
Project 16 48
Midterms 3 15
Final 7 35
Total Workload 196

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution