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Advisor : Dr. Hsu Presenter : Ai-Chen Liao Authors : Yiu-ming Cheung

On R ival P enalization C ontrolled C ompetitive L earning for Clustering with Automatic Cluster Number Selection. Advisor : Dr. Hsu Presenter : Ai-Chen Liao Authors : Yiu-ming Cheung. 2005 . TKDE . Page(s) : 1583 - 1588. Outline. Motivation Objective Method RPCL

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Advisor : Dr. Hsu Presenter : Ai-Chen Liao Authors : Yiu-ming Cheung

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  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. Outline • Motivation • Objective • Method • RPCL • RPCCL • Experimental Results • Conclusion • Personal Opinions

  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. 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. 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. cluster center each input unchanged Rival (move away) Winner (move closer) Method ─ RPCL

  7. Method ─ RPCL

  8. Method ─ RPCCL

  9. compare Method ─ RPCCL This penalization control mechanism by with

  10. Experimental Results RPCL : learning rate αC at 0.001, and αr at 0.0001 the number of seed points : 30 audience image : 128*128 pixels epoch :50 RPCL RPCCL original Audience Image

  11. Conclusion • RPCCL has novelly circumvented the difficult selection of the deleaning rate.

  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. 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. Heuristic Frequency Sensitive Competitive Learning (FSCL) algorithm Dead-unit 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

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