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Quality of Clusterings

Quality of Clusterings. Two metrics: SSE Dissimilarity Ratio. Computing SSE. Save clusters. Two new columns are created: Cluster and Distance . Create new column as formula. Name it as dist-sqr and define it as Distance 2

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Quality of Clusterings

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  1. Quality of Clusterings • Two metrics: • SSE • Dissimilarity Ratio

  2. Computing SSE • Save clusters. Two new columns are created: Cluster and Distance. • Create new column as formula. Name it as dist-sqr and define it as Distance2 • Analyze – Distribution for dist-sqr. Get the mean and multiply by N to obtain SSE

  3. Computing Dissimilarity Ratio • Dissimilarity ratio = (inter-cluster distance / intra-cluster distance) • Inter-cluster distance is the smallest distance between centroids • Normalize centroid coordinates: • Coordinates are given in cluster output • Find mean and std dev for each dimension from histogram (distribution) output • Normalize each centroid coordinate: • (x - mean) /st dev • Compute distances between each pair of centroids: • Inter-cluster distance is given by the smallest of the normalized centroid distances

  4. Dissimilarity Ratio – cont. • Intra-cluster distance is given by the average max dist of the clusters. • The max dist of each cluster is found at the clusters output in JMP. • Computer dissimilarity ratio (DR) for each clustering • The higher the DR the better the clustering.

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