Measure the Quality of Clustering Dissimilarity/Similarity metric Similarity is expressed in terms of a distance function typically metric: diD 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 subjectiveMeasure 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 10