Information Package / Course Catalogue
Advanced Data Mining
Course Code: MCS515
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

The aim of this course is to enable students to learn advanced concepts, methods, and algorithms in the field of data mining both theoretically and practically. Within the scope of the course, students are expected to extract meaningful patterns, relationships, and knowledge from large and complex data sets and to use basic and advanced data mining techniques such as classification, clustering, association rules, dimensionality reduction, anomaly detection, and prediction. In addition, the course aims to develop students' abilities to select appropriate data mining methods for different problem domains, build models, evaluate model performance, and interpret the obtained results from a scientific and analytical perspective.

Course Content

In summary, the course aims to help students gain competence in data preprocessing, feature selection, model optimization, and the use of results in decision-support processes. In addition, through current data mining approaches, machine learning-based methods, and real-world applications, the course aims to strengthen students' abilities to conduct research, solve problems, and perform academic-level data analysis

Name of Lecturer(s)
Learning Outcomes
1.Explain the data mining process at an advanced level and interpret the stages of data preprocessing, data cleaning, feature selection, dimensionality reduction, modeling, and evaluation of results holistically.
2.Apply data mining methods such as classification, clustering, association rules, anomaly detection, and prediction; select appropriate algorithms for different problem types and use them on real data sets.
3.Evaluate the performance of machine learning and advanced data mining algorithms; compare models by using measures such as accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, cross-validation, and error analysis.
4.Analyze large, complex, imbalanced, textual, or time-dependent data sets; determine methods appropriate to the data structure and obtain meaningful patterns, relationships, and knowledge inferences.
5.Interpret data mining results from an academic, ethical, and decision-support-oriented perspective; evaluate model output explainability, algorithmic bias, data privacy, and limitations within the application context.
Recommended or Required Reading
1.Han, J., Pei, J., & Tong, H. (2022). Data Mining: Concepts and Techniques (4th ed.). Morgan Kaufmann / Elsevier.
2.Zaki, M. J., & Meira, W. Jr. (2020). Data Mining and Machine Learning: Fundamental Concepts and Algorithms (2nd ed.). Cambridge University Press.
3.Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer.
4.Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Data Mining and the Scope of Advanced Data Mining: Basic concepts of data mining, the knowledge discovery process, the relationship between data mining and machine learning, application areas, and current research trends
Week 2 - Theoretical
Data Preprocessing and Data Quality: Missing data analysis, identification of outliers, data cleaning, normalization, standardization, data transformation, and data integration methods.
Week 3 - Theoretical
Feature Selection and Dimensionality Reduction Methods: Filter, wrapper, and embedded feature selection approaches; various dimensionality reduction methods such as PCA, LDA, t-SNE, and UMAP, and the presentation of newly used algorithms for this purpose.
Week 4 - Theoretical
Classification Methods I: Decision trees, Naive Bayes, k-nearest neighbors, logistic regression, and support vector machines; model building and interpretation of decision boundaries.
Week 5 - Theoretical
Classification Methods II and Ensemble Learning: Random Forest, Bagging, Boosting, AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost algorithms.
Week 6 - Theoretical
Model Evaluation and Validation Techniques: Training-test split, cross-validation, confusion matrix, accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, and model comparison approaches.
Week 7 - Theoretical
Imbalanced Data Problems and Cost-Sensitive Learning: Class imbalance, oversampling, undersampling, SMOTE, ADASYN, class weighting, and cost-sensitive classification methods.
Week 8 - Theoretical
Clustering Methods: K-Means, hierarchical clustering, DBSCAN, OPTICS, Gaussian Mixture Models; cluster validity measures and interpretation of clustering results.
Week 9 - Theoretical
Association Rules and Pattern Mining: Apriori, FP-Growth, support, confidence, lift, frequent itemset mining, and market basket analysis applications.
Week 10 - Theoretical
Outlier and Anomaly Detection: Statistical methods, distance-based methods, density-based approaches, Isolation Forest, Local Outlier Factor, and anomaly detection applications.
Week 11 - Theoretical
Text Mining and Fundamentals of Natural Language Processing: Text preprocessing, word embedding methods, topic modeling, sentiment analysis, and text classification applications.
Week 12 - Theoretical
Time Series, Data Stream, and Big Data Mining: Time-series patterns, data stream mining, concept drift, data mining in big data environments, and scalable algorithms.
Week 13 - Theoretical
Explainable Artificial Intelligence, Model Interpretability, and Ethical Dimensions: Feature importance, SHAP, LIME, model explainability, algorithmic bias, data privacy, and ethical data mining.
Week 14 - Theoretical
Project Presentations
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%10
Quiz1%5
Midterm Examination1%15
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Assignment110313
Individual Work142028
Quiz1516
Midterm Examination110313
Final Examination115318
TOTAL WORKLOAD (hours)148
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
OÇ-1
3
3
3
3
5
3
5
3
3
OÇ-2
4
4
4
3
5
4
3
4
3
OÇ-3
3
3
5
5
5
4
3
5
5
OÇ-4
5
4
5
4
4
4
5
5
4
OÇ-5
3
3
3
3
4
3
4
3
3
Adnan Menderes University - Information Package / Course Catalogue
2026