Data Mining and Knowledge Discovery

8.

Advanced Mining Techniques

8.1.

Anomaly Detection

8.1.1.

8.1.1.1.

Point Anomalies

8.1.1.2.

Contextual Anomalies

8.1.1.3.

Collective Anomalies

8.1.2.

Statistical Approaches

8.1.2.1.

Parametric Methods

8.1.2.2.

Non-Parametric Methods

8.1.2.3.

Hypothesis Testing

8.1.3.

Machine Learning Approaches

8.1.3.1.

Supervised Anomaly Detection

8.1.3.2.

Unsupervised Anomaly Detection

8.1.3.3.

Semi-Supervised Methods

8.1.4.

Proximity-Based Detection

8.1.4.1.

Distance-Based Methods

8.1.4.2.

Density-Based Methods

8.1.4.3.

Clustering-Based Detection

8.1.5.

Information-Theoretic Approaches

8.1.6.

Spectral Anomaly Detection

8.2.

Text Mining and Natural Language Processing

8.2.1.

Text Preprocessing

8.2.1.1.

8.2.1.2.

Stop Word Removal

8.2.1.3.

Stemming and Lemmatization

8.2.1.4.

Part-of-Speech Tagging

8.2.2.

Text Representation

8.2.2.1.

Bag-of-Words Model

8.2.2.2.

TF-IDF Weighting

8.2.2.3.

8.2.2.4.

Word Embeddings

8.2.3.

Document Classification

8.2.4.

Document Clustering

8.2.5.

8.2.5.1.

Latent Semantic Analysis

8.2.5.2.

Latent Dirichlet Allocation

8.2.5.3.

Non-Negative Matrix Factorization

8.2.6.

Sentiment Analysis

8.2.6.1.

Lexicon-Based Approaches

8.2.6.2.

Machine Learning Methods

8.2.6.3.

Deep Learning Approaches

8.2.7.

Information Extraction

8.2.7.1.

Named Entity Recognition

8.2.7.2.

Relation Extraction

8.2.7.3.

Event Extraction

8.3.

Time Series Mining

8.3.1.

Time Series Characteristics

8.3.2.

8.3.2.1.

Moving Averages

8.3.2.2.

Exponential Smoothing

8.3.2.3.

Seasonal Decomposition

8.3.3.

Time Series Similarity

8.3.3.1.

Distance Measures

8.3.3.2.

Dynamic Time Warping

8.3.3.3.

Longest Common Subsequence

8.3.4.

Time Series Classification

8.3.5.

Time Series Clustering

8.3.6.

Sequential Pattern Mining

8.3.6.1.

Apriori-Based Methods

8.3.6.2.

Pattern-Growth Methods

8.3.6.3.

Constraint-Based Mining

8.3.7.

Time Series Forecasting

8.4.

Spatial Data Mining

8.4.1.

Spatial Data Types

8.4.2.

Spatial Relationships

8.4.3.

Spatial Clustering

8.4.3.1.

Density-Based Methods

8.4.3.2.

Hierarchical Methods

8.4.4.

Spatial Association Rules

8.4.5.

Spatial Outlier Detection

8.4.6.

Spatial Classification

8.4.7.

Geographic Information Systems Integration

8.5.

Graph Mining and Social Network Analysis

8.5.1.

Graph Representation

8.5.2.

Graph Properties

8.5.2.1.

Degree Distribution

8.5.2.2.

Clustering Coefficient

8.5.2.3.

8.5.3.

Community Detection

8.5.3.1.

Modularity Optimization

8.5.3.2.

Spectral Methods

8.5.3.3.

Label Propagation

8.5.4.

8.5.4.1.

PageRank Algorithm

8.5.4.2.

8.5.4.3.

Centrality Measures

8.5.5.

Link Prediction

8.5.5.1.

Similarity-Based Methods

8.5.5.2.

Probabilistic Models

8.5.5.3.

Machine Learning Approaches

8.5.6.

Influence Analysis

8.5.6.1.

Influence Maximization

8.5.6.2.

Information Diffusion Models

8.5.7.

Dynamic Network Analysis

8.6.

8.6.1.

Web Content Mining

8.6.1.1.

Text Extraction

8.6.1.2.

Multimedia Content Mining

8.6.1.3.

Semantic Web Mining

8.6.2.

Web Structure Mining

8.6.2.1.

8.6.2.2.

Web Graph Properties

8.6.2.3.

Authority and Hub Identification

8.6.3.

Web Usage Mining

8.6.3.1.

Web Log Analysis

8.6.3.2.

Clickstream Mining

8.6.3.3.

User Session Analysis

8.6.3.4.

Web Personalization

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9. Model Evaluation and Validation