cs345-cl2new

# cs345-cl2new - More Clustering CURE Algorithm Clustering...

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1 More Clustering CURE Algorithm Clustering Streams

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2 The CURE Algorithm r Problem with BFR/ k -means: R Assumes clusters are normally distributed in each dimension. R And axes are fixed --- ellipses at an angle are not OK. r CURE: R Assumes a Euclidean distance. R Allows clusters to assume any shape.
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.
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.
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 r Now, visit each point p in the data set. r Place it in the “closest cluster.” R Normal definition of “closest”: that cluster with the closest (to ) among all the sample points of all the clusters.
9 Clustering a Stream ( New Topic ) r Assume points enter in a stream. r

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## This note was uploaded on 01/31/2011 for the course CS 345 taught by Professor Dunbar,a during the Fall '07 term at UC Davis.

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cs345-cl2new - More Clustering CURE Algorithm Clustering...

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