What is clustering?

Partitional clustering

Hierarchical clustering

Clustering algorithms

(3 used in this course)

Clustering distinctions

1. Exclusive versus non-exclusive
2. Fuzzy versus non-fuzzy
3. Partial versus complete
4. Heterogeneous versus homogeneous

Centroid

K - means complexity

Well-separated cluster

Center-based cluster

Contiguity- based cluster

Density-based cluster

Shared property clusters
(conceptual clusters)

Pre-processing and post-processing
(clustering)

Strengths of hierarchical clustering

Two main types of hierarchical clustering

Cluster similarity - MIN or Single Link

Cluster similarity - MAX or Complete Link

Cluster similarity - Group average

Cluster similarity - Ward's method

DBSCAN - density

DBSCAN - MinPts

DBSCAN - Border

DBSCAN - Noise

DBSCAN - pros and cons