1. Introduction to Data Mining
2. Data Preprocessing MCQs
- Data cleaning and integration MCQs
- Data transformation MCQs
- Data reduction techniques MCQs
- Handling missing data MCQs
3. Data Mining Techniques MCQs
- Classification MCQs
- Clustering MCQs
- Regression MCQs
- Association rule mining MCQs
- Anomaly detection MCQs
4. Classification Techniques MCQs
- Decision trees MCQs
- Naive Bayes MCQs
- k-Nearest Neighbors (k-NN) MCQs
- Support Vector Machines (SVM) MCQs
- Neural networks MCQs
5. Clustering Techniques MCQs
- k-Means clustering MCQs
- Hierarchical clustering MCQs
- Density-based clustering (e.g., DBSCAN) MCQs
- Cluster evaluation MCQs
6. Association Rule Mining MCQs
- Apriori algorithm MCQs
- FP-growth algorithm MCQs
- Measures of interestingness (e.g., support, confidence, lift) MCQs
7. Data Warehousing and OLAP MCQs
- Concepts of data warehouse MCQs
- OLAP operations (e.g., slice, dice, roll-up, drill-down) MCQs
- Multidimensional data models MCQs
8. Web Mining MCQs
9. Text Mining and Natural Language Processing (NLP) MCQs
10. Data Mining Tools and Frameworks MCQs
11. Evaluation and Validation MCQs
12. Big Data and Data Mining MCQs
- Challenges in mining large datasets MCQs
- Distributed data mining MCQs
- Role of Hadoop and MapReduce MCQs
13. Privacy and Ethical Issues in Data Mining MCQs
- Data security concerns MCQs
- Ethical considerations (e.g., consent, bias) MCQs
- Legal regulations (e.g., GDPR, HIPAA) MCQs