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Measure the Quality of Clustering

• Dissimilarity/Similarity metric
• Similarity is expressed in terms of a distance function, typically metric: d(i, j)
• The definitions of distance functions are usually rather different for interval-scaled, boolean, categorical, ordinal ratio, and vector variables
• Weights should be associated with different variables based on applications and data semantics
• Quality of clustering:
• There is usually a separate “quality” function that measures the “goodness” of a cluster.
• It is hard to define “similar enough” or “good enough”
• The answer is typically highly subjective