Data Mining and Knowledge Discovery

6.

Clustering Analysis

6.1.

Clustering Fundamentals

6.1.1.

Unsupervised Learning Principles

6.1.2.

Cluster Types and Characteristics

6.1.3.

Similarity and Distance Measures

6.1.4.

Cluster Validation Concepts

6.2.

Partitioning Clustering Methods

6.2.1.

K-Means Clustering

6.2.1.1.

Algorithm Steps

6.2.1.2.

Initialization Strategies

6.2.1.3.

Convergence Criteria

6.2.1.4.

Choosing Optimal K

6.2.2.

K-Medoids Clustering

6.2.2.1.

6.2.2.2.

CLARA Algorithm

6.2.2.3.

Robustness Properties

6.2.3.

6.2.3.1.

Fuzzy Membership

6.2.3.2.

Algorithm Details

6.2.3.3.

Parameter Selection

6.3.

Hierarchical Clustering Methods

6.3.1.

Agglomerative Clustering

6.3.1.1.

6.3.1.2.

Complete Linkage

6.3.1.3.

Average Linkage

6.3.1.4.

6.3.2.

Divisive Clustering

6.3.2.1.

Top-Down Approaches

6.3.2.2.

Splitting Strategies

6.3.3.

Dendrogram Construction

6.3.4.

Cutting Dendrograms

6.4.

Density-Based Clustering

6.4.1.

DBSCAN Algorithm

6.4.1.1.

6.4.1.2.

6.4.1.3.

6.4.1.4.

Parameter Selection

6.4.2.

OPTICS Algorithm

6.4.2.1.

Reachability Distance

6.4.2.2.

6.4.2.3.

Reachability Plot

6.4.3.

Mean Shift Clustering

6.5.

Grid-Based Clustering

6.5.1.

STING Algorithm

6.5.2.

CLIQUE Algorithm

6.5.3.

Grid Structure Design

6.6.

Model-Based Clustering

6.6.1.

Gaussian Mixture Models

6.6.2.

Expectation-Maximization Algorithm

6.6.3.

Model Selection Criteria

6.7.

Cluster Evaluation

6.7.1.

Internal Validation Measures

6.7.1.1.

Silhouette Coefficient

6.7.1.2.

Davies-Bouldin Index

6.7.1.3.

Calinski-Harabasz Index

6.7.2.

External Validation Measures

6.7.2.1.

6.7.2.2.

Adjusted Rand Index

6.7.2.3.

Normalized Mutual Information

6.7.3.

Stability Analysis

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7. Association Rule Mining