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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
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