cs345-cl2new - MoreClustering CUREAlgorithm...

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1 More Clustering CURE Algorithm Clustering Streams
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2 The CURE Algorithm Problem with BFR/ k  -means: Assumes clusters are normally distributed  in each dimension. And axes are fixed --- ellipses at an angle  are  not   OK. CURE: Assumes a Euclidean distance. Allows clusters to assume any shape.
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3 Example: Stanford Faculty Salaries e e e e e e e e e e e h h h h h h h h h h h h h salary age
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4 Starting CURE 1. Pick a random sample of points that fit  in main memory. 2. Cluster these points hierarchically ---  group nearest points/clusters. 3. For each cluster, pick a sample of  points, as dispersed as possible. 4. From the sample, pick representatives  by moving them (say) 20% toward the  centroid of the cluster.
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5 Example : Initial Clusters e e e e e e e e e e e h h h h h h h h h h h h h salary age
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6 Example : Pick Dispersed Points e e e e e e e e e e e h h h h h h h h h h h h h salary age Pick (say) 4 remote points for each cluster.
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7 Example : Pick Dispersed Points e e e e e e e e e e e h h h h h h h h h h h h h salary age Move points (say) 20% toward the centroid.
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8 Finishing CURE Now, visit each point  p   in the data set. Place it in the “closest cluster.” Normal definition of “closest”: that cluster  with the closest (to  p  ) among all the  sample points of all the clusters.
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9 Clustering a Stream ( New Topic ) Assume points enter in a stream. Maintain a sliding window of points. Queries ask for clusters of points within  some suffix of the window.
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