Data Mining B.Tech Handwritten Notes For University Exams

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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