1. Local Clustering Algorithm DISCOVIR 9-16-2002

2. Current Situation • Image collection within a client is modeled as a single cluster.

3. Proposed Improvement • Multiple clusters exist in the image collection

4. Group of similar local cluster A

5. Group of similar local cluster B

6. Group of similar local cluster C

7. Clustering Algorithm • 3 clustering algorithms are proposed and tested • C – set of cluster center • X – dataset • Goal of clustering : minimize Error

8. Randomly pick a point from dataset X Find closest clustercenter c and update Error change < thresholditerate a certain step? Y N Returncluster center Procedure • Randomly pick k xj from {X} and assign them as the set {C} as initial cluster center. Input datasetand cluster

9. Shifting Mean (SM) • Suppose xj is picked and ci is the closest cluster center • Let p be number of times ci wins, initially p=1 • Update ci by

10. Competitive Learning (CL) • Update ci by • t – the current number of iteration so far • T – total number of iteration intend to run • We choose  by 0.5, 0.3, 0.1

11. Illustration ci xj

12. Illustration ci xj Winner (move closer)

13. Rival Penalized Competitive Learning (RPCL) • Suppose cl is the second closest cluster center to xj • Update ci by • Update cl by • We choose  = 0.05

14. Illustration ci xj unchanged Rival (move away) Winner (move closer)

15. Final Steps • For each xj ,find the closest ci and mark xj belongs to ci • Calculate error function • Carryout experiments by varying # of iteration, learning rate

16. Results Fixed Learning rate Varying iteration Fixed Learning rate Varying iteration Fixed IterationVarying learning rate

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20. Others • Other variation • i – initial learning rate • f – final learning rate • Interesting link for competitive learningsome competitive learning methods