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Computer Science
Data Science
Data Mining and Knowledge Discovery
1. Introduction to Data Mining and Knowledge Discovery
2. Data Types and Sources
3. Data Preprocessing Fundamentals
4. Classification Methods
5. Regression Analysis
6. Clustering Analysis
7. Association Rule Mining
8. Advanced Mining Techniques
9. Model Evaluation and Validation
10. Model Interpretation and Explainability
11. Deployment and Production Systems
12. Ethics, Privacy, and Security
Association Rule Mining
Association Analysis Fundamentals
Market Basket Analysis
Itemset Concepts
Support and Confidence Measures
Lift and Interest Measures
Rule Quality Assessment
Frequent Itemset Mining
Apriori Algorithm
Candidate Generation
Support Counting
Pruning Strategies
Algorithm Optimization
FP-Growth Algorithm
FP-Tree Construction
Conditional Pattern Bases
Recursive Mining
Eclat Algorithm
Vertical Data Format
Intersection-Based Mining
Alternative Algorithms
AprioriTid
DHP Algorithm
Sampling-Based Methods
Association Rule Generation
Rule Extraction from Frequent Itemsets
Confidence-Based Filtering
Lift-Based Evaluation
Rule Ranking Methods
Advanced Association Mining
Multi-Level Association Rules
Multi-Dimensional Association Rules
Quantitative Association Rules
Sequential Pattern Mining
Constraint-Based Mining
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6. Clustering Analysis
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8. Advanced Mining Techniques