Current Slide

Considerations for Cluster Analysis

• Partitioning criteria
• Single level vs. hierarchical partitioning (often, multi-level hierarchical partitioning is desirable)
• Separation of clusters
• Exclusive (e.g., one customer belongs to only one region) vs. non-exclusive (e.g., one document may belong to more than one class)
• Similarity measure
• Distance-based (e.g., Euclidian, road network, vector) vs. connectivity-based (e.g., density or contiguity)
• Clustering space
• Full space (often when low dimensional) vs. subspaces (often in high-dimensional clustering)