cs345-cl2new

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

Info iconThis preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon
1 More Clustering CURE Algorithm Clustering Streams
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
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.
Background image of page 2
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
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
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.
Background image of page 4
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
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
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.
Background image of page 6
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.
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
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.
Background image of page 8
9 Clustering a Stream ( New Topic ) r Assume points enter in a stream. r
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 10
This is the end of the preview. Sign up to access the rest of the document.

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.

Page1 / 28

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

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online