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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
Clustering Analysis
Clustering Fundamentals
Unsupervised Learning Principles
Cluster Types and Characteristics
Similarity and Distance Measures
Cluster Validation Concepts
Partitioning Clustering Methods
K-Means Clustering
Algorithm Steps
Initialization Strategies
Convergence Criteria
Choosing Optimal K
K-Medoids Clustering
PAM Algorithm
CLARA Algorithm
Robustness Properties
Fuzzy C-Means
Fuzzy Membership
Algorithm Details
Parameter Selection
Hierarchical Clustering Methods
Agglomerative Clustering
Single Linkage
Complete Linkage
Average Linkage
Ward's Method
Divisive Clustering
Top-Down Approaches
Splitting Strategies
Dendrogram Construction
Cutting Dendrograms
Density-Based Clustering
DBSCAN Algorithm
Core Points
Border Points
Noise Points
Parameter Selection
OPTICS Algorithm
Reachability Distance
Core Distance
Reachability Plot
Mean Shift Clustering
Grid-Based Clustering
STING Algorithm
CLIQUE Algorithm
Grid Structure Design
Model-Based Clustering
Gaussian Mixture Models
Expectation-Maximization Algorithm
Model Selection Criteria
Cluster Evaluation
Internal Validation Measures
Silhouette Coefficient
Davies-Bouldin Index
Calinski-Harabasz Index
External Validation Measures
Rand Index
Adjusted Rand Index
Normalized Mutual Information
Stability Analysis
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7. Association Rule Mining