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 8Considerations 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) 8