Faculty of Engineering and Natural Sciences · Computer Engineering · Undergraduate
Course Objective
The aim of this course is to enable students to understand the concepts, processes, and techniques of data mining. Students will gain skills in knowledge discovery, data analysis, and developing decision support systems in various application areas using data mining techniques.
Course Content
It includes topics such as data preprocessing, classification, clustering, association rules, sequential patterns, text mining, social networks, and an introduction to web mining.
Required Resources
Lecture Notes
Jiawei Han, Micheline Kamber & Jian Pei. Data Mining: Concepts and Techniques, 3rd Edition. Morgan Kaufmann Publishers,2011 ISBN-13: 978-0123814791
Recommended Resources
E. Alpaydin. Introduction to Machine Learning, 4th ed., MIT Press, 2020.
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000 T.
Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009 B.
Liu, Web Data Mining, Springer 2006 P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005
S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998
I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005
Course Learning Outcomes
- Identify the fundamental steps of knowledge discovery and data analysis processes, analyze data using appropriate algorithms, and evaluate the results.
- Perform data cleaning, data integration and transformation, data reduction, and feature extraction.
- Develop data mining models such as classification, clustering, and regression, and apply them to datasets by selecting suitable methods.
- Apply data mining techniques (e.g., classification, clustering, association rules) to various fields such as marketing, healthcare, and finance, and evaluate the significance of these rules.
- Present results using comprehensible visuals and reports, and explain findings to relevant stakeholders.
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 3 | 48 |
| Out of Class Study Period | 16 | 2 | 32 |
| Midterm | 1 | 20 | 20 |
| Quiz | 0 | 0 | 0 |
| Assignment | 1 | 25 | 25 |
| Practice | 0 | 0 | 0 |
| Final | 1 | 25 | 25 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Introduction to Data Mining | Ch 1 |
| 2 | Data Preprocessing | Ch 2-3-4 |
| 3 | Data Preprocessing | Ch 2-3-4 |
| 4 | Classification Techniques | Ch 6 |
| 5 | Classification Techniques (Continue'd) | Ch 6 |
| 6 | Clustering Techniques | Ch 7 |
| 7 | Clustering Techniques (Continue'd) | Ch 7 |
| 8 | Midterm Exam | Ara Sınav |
| 9 | Advanced Clustering Techniques | Ch 7 |
| 10 | Association Rule Mining | Ch 5 |
| 11 | Advanced Association Rule Mining | Ch 5 |
| 12 | Text Mining | - |
| 13 | Social Networks | - |
| 14 | Web Mining | - |
| 15 | Web Mining | - |
| 16 | Final Exam | Final Sınavı |