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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)

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