1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 On Rival Penalization Controlled Competitive Learning for Clustering with Automatic Cluster Number Selection Advisor : Dr. Hsu Presenter : Ai-Chen Liao Authors : Yiu-ming Cheung 2005. TKDE. Page(s) : 1583 - 1588

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Method  RPCL  RPCCL Experimental Results Conclusion Personal Opinions

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation  K-means algorithm has at least two major drawbacks: ─ It suffers from the dead-unit problem. ─ If the number of clusters is misspecified, i.e., k is not equal to the true cluster number k*, the performance of k-means algorithm deteriorates rapidly.  The performance of RPCL is sensitive to the value of the delearning rate.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective  We will concentrate on studying the RPCL algorithm and propose a novel technique to circumvent the selection of the delearning rate.  We further investigate the RPCL and present a mechanism to control the strength of rival penalization dynamically.

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Method ─ RPCL  Advantage : RPCL can automatically select the correct cluster number by gradually driving redundant seed points far away from the input dense regions.  Drawback : RPCL is sensitive to the delearning rate.  Idea : ex. In a election campaign…..(more intense)….. candidates : A  40% B  35% C  5%

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Method ─ RPCL cluster center each input Winner (move closer) Rival (move away) unchanged

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Method ─ RPCL

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Method ─ RPCCL

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Method ─ RPCCL This penalization control mechanism by with compare

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Experimental Results RPCL : learning rate α C at 0.001, and αr at 0.0001 the number of seed points : 30 audience image : 128*128 pixels epoch :50 original Audience Image RPCL RPCCL

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Conclusion  RPCCL has novelly circumvented the difficult selection of the deleaning rate.

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Personal Opinions Advantage  RPCCL can automatically select the correct cluster number.  The novel technique can circumvent the selection of the delearning rate. Drawback  limitation : k >= k* Application  clustering…

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 13 K-means example 1. Given : {2,4,10,12,3,20,30,11,25} k=2 2. Randomly assign means : m1=3 ; m2=4  k1={2,3}, k2={4,10,12,20,30,11,25},m1=2.5, m2=16  k1={2,3,4}, k2={10,12,20,30,11,25}, m1=3, m2=18  k1={2,3,4,10}, k2={12,20,30,11,25}, m1=4.75, m2=19.6  k1={2,3,4,10,11,12}, k2={20,30,25}, m1=7, m2=25 …..

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 14 Dead-unit problem 1. Given : {2,4,10,12,3,20,30,11,25}, k=3 2. Randomly assign means : m1=30 ; m2=25 ; m3=10 Dead-unit Heuristic Frequency Sensitive Competitive Learning (FSCL) algorithm