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1. Introduction to Data Mining
- Definition and significance of data mining
- Data mining vs. data warehousing
- Knowledge discovery in databases (KDD) process
- Applications of data mining in various domains
- Challenges and trends in data mining
2. Data Preprocessing
- Data cleaning: Handling missing values, noisy data, outliers
- Data integration and transformation
- Data reduction: Dimensionality reduction, PCA
- Data discretization and concept hierarchy generation
- Feature selection and extraction
- Data visualization techniques
3. Data Mining Techniques
- Overview of predictive and descriptive data mining
- Classification vs. clustering vs. association analysis
4. Classification
- Decision tree induction
- Bayesian classification: Naive Bayes
- Rule-based classification
- Support Vector Machines (SVM)
- Neural networks and deep learning
- Evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC curves
5. Clustering
- Partitioning methods: k-Means, k-Medoids
- Hierarchical clustering: Agglomerative and divisive approaches
- Density-based clustering: DBSCAN, OPTICS
- Grid-based clustering: STING
- Model-based clustering: Expectation-Maximization (EM)
- Evaluation of clustering results: Silhouette coefficient, Davies-Bouldin Index
6. Association Rule Mining
- Basics of association rules: Support, Confidence, Lift
- Apriori algorithm
- FP-Growth algorithm
- Mining multilevel and multidimensional association rules
- Applications of association rules in market basket analysis
7. Advanced Data Mining Topics
- Sequential pattern mining
- Temporal data mining
- Text mining and natural language processing (NLP)
- Web mining: Web content, structure, and usage mining
- Spatial and spatiotemporal data mining
- Multimedia data mining
8. Data Mining Tools and Techniques
- Overview of popular data mining tools: Weka, RapidMiner, KNIME, Orange, R, Python (pandas, scikit-learn)
- Data mining with SQL
- Data mining using big data platforms: Hadoop, Spark
9. Data Mining and Machine Learning
- Relationship between data mining and machine learning
- Supervised vs. unsupervised learning
- Semi-supervised and reinforcement learning
- Feature engineering and selection in machine learning
10. Data Mining and Privacy
- Ethical issues in data mining
- Data security and privacy concerns
- Privacy-preserving data mining techniques
- Legal and social implications of data mining
11. Case Studies and Applications
- Fraud detection in banking
- Customer segmentation in marketing
- Sentiment analysis in social media
- Recommendation systems (e.g., Netflix, Amazon)
- Healthcare analytics: Disease prediction and diagnosis
- Industrial applications: Predictive maintenance
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