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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
Advanced Mining Techniques
Anomaly Detection
Anomaly Types
Point Anomalies
Contextual Anomalies
Collective Anomalies
Statistical Approaches
Parametric Methods
Non-Parametric Methods
Hypothesis Testing
Machine Learning Approaches
Supervised Anomaly Detection
Unsupervised Anomaly Detection
Semi-Supervised Methods
Proximity-Based Detection
Distance-Based Methods
Density-Based Methods
Clustering-Based Detection
Information-Theoretic Approaches
Spectral Anomaly Detection
Text Mining and Natural Language Processing
Text Preprocessing
Tokenization
Stop Word Removal
Stemming and Lemmatization
Part-of-Speech Tagging
Text Representation
Bag-of-Words Model
TF-IDF Weighting
N-Gram Models
Word Embeddings
Document Classification
Document Clustering
Topic Modeling
Latent Semantic Analysis
Latent Dirichlet Allocation
Non-Negative Matrix Factorization
Sentiment Analysis
Lexicon-Based Approaches
Machine Learning Methods
Deep Learning Approaches
Information Extraction
Named Entity Recognition
Relation Extraction
Event Extraction
Time Series Mining
Time Series Characteristics
Trend Analysis
Moving Averages
Exponential Smoothing
Seasonal Decomposition
Time Series Similarity
Distance Measures
Dynamic Time Warping
Longest Common Subsequence
Time Series Classification
Time Series Clustering
Sequential Pattern Mining
Apriori-Based Methods
Pattern-Growth Methods
Constraint-Based Mining
Time Series Forecasting
Spatial Data Mining
Spatial Data Types
Spatial Relationships
Spatial Clustering
Density-Based Methods
Hierarchical Methods
Spatial Association Rules
Spatial Outlier Detection
Spatial Classification
Geographic Information Systems Integration
Graph Mining and Social Network Analysis
Graph Representation
Graph Properties
Degree Distribution
Clustering Coefficient
Path Length
Community Detection
Modularity Optimization
Spectral Methods
Label Propagation
Link Analysis
PageRank Algorithm
HITS Algorithm
Centrality Measures
Link Prediction
Similarity-Based Methods
Probabilistic Models
Machine Learning Approaches
Influence Analysis
Influence Maximization
Information Diffusion Models
Dynamic Network Analysis
Web Mining
Web Content Mining
Text Extraction
Multimedia Content Mining
Semantic Web Mining
Web Structure Mining
Link Analysis
Web Graph Properties
Authority and Hub Identification
Web Usage Mining
Web Log Analysis
Clickstream Mining
User Session Analysis
Web Personalization
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7. Association Rule Mining
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9. Model Evaluation and Validation