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lect8 - Clustering V Outline Validating clustering results...

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Clustering V
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Outline Validating clustering results Randomization tests
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Cluster Validity All clustering algorithms provided with a set of points output a clustering How to evaluate the “goodness” of the resulting clusters? Tricky because “clusters are in the eye of the beholder”! Then why do we want to evaluate them? To compare clustering algorithms To compare two sets of clusters To compare two clusters To decide whether there is noise in the data
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Clusters found in Random Data 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x y Random Points 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x K-means 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x DBSCAN 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x Complete Link
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Use the objective function F Dataset X , Objective function F Algorithms: A 1 , A 2 ,…A k Question: Which algorithm is the best for this objective function? R 1 = A 1 (X), R 2 = A 2 (X),…,R k =A k (X) Compare F(R 1 ), F(R 2 ),…,F(R k )
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Evaluating clusters Function H computes the cohesiveness of a cluster (e.g., smaller values larger cohesiveness) Examples of cohesiveness? Goodness of a cluster c is H(c) c is better than c’ if H(c) < H(c’)
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Evaluating clusterings using cluster cohesiveness? For a clustering C consisting of k clusters c 1 ,…,c k H(C) = Φ i H(c i ) What is Φ ?
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Cluster separation? Function S that measures the separation between two clusters c i , c j Ideas for S(c i ,c j )? How can we measure the goodness of a clustering C = {c 1 ,…,c k } using the separation function S ?
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Silhouette Coefficient combines ideas of both cohesion and separation, but for individual points, as well as clusters and clusterings For an individual point, I a
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lect8 - Clustering V Outline Validating clustering results...

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