1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Enhanced neural gas network for prototype-based clustering Presenter : Shao-Wei Cheng Authors : A.K. Qin, P.N. Suganthan PR 2005

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Methodology Experiments and Results Conclusion Personal Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation There are several problems about PBC and NG algorithm. Sensitivity to initialization. Sensitivity to input sequence ordering The adverse influence from outliers......

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objectives Present an improved PBC algorithm based on the enhanced NG network framework, called the ENG. Tackle several problems about PBC......

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Methodology - original PBC algorithms ： k-means, fuzzy k-means NG network algorithms ： single-layered neural network  Faster convergence to low distortion errors.  Lower distortion error than other methods.  Obeying a stochastic gradient descent. The original NG algorithm The original NG algorithm with concept of fuzzy..... V. 0 12 3 4

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology - enhanced Enhanced NG network framework  (3) – Explain the influence of outlier, updating from Eq. (1)  (4) – The new formula updating from Eq. (3)  (5) 、 (6) 、 (7) – Explain the parameters in Eq. (4)

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – MDL framework MDL principle is employed as the performance measure. Original MDL MDL in this approach as

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology - processes Initialize (a) If m < Max_epoch (b) If trainingset is not empty (c) If training stage is at RP_epoch (d) For j = 1 to size(V) (e) For j = 1 to size(Torelocate) (f) If current utifactor value < previous utifactor value Training epoch += 1End Draw data in training set and compute Y N Y N Y N Y N change restore c synaptic weights W = {w 1,w 2,...,w c } randomly is the middle value for to control the acceleration of ’s changing κ and η are the parameters used to calculate the MDL value The initial training epoch number: m = 0 The initial iteration step number t in training epoch m : t = 1 Total iteration step number iter is ： iter = m · N + t The maximum training epoch is set as Max_epoch The dislocated prototypes’ relocation is defined as RP_epoch The dataset for training is V = {v 1, v 2,..., v N }

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology - processes

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Methodology - processes

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology - processes

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Compared to 9 algorithms ： HCM, FCM, NG, FPCM, CFCM-F1, CFCM-F2, HRC-FRC, AHCM, and AFCM. Data set ：  Artificial – D 1, D 2  UCI datasets Run each clustering algorithms 10 times. Parameter settings ：  ε i =0.8, ε f =0.05 ； λ i =10, λ f =0.01 ； β i =50, β m = 10, β f = 0.01  κ= 2, η= 1 e − 4  Max_epoch = 10, RP_epoch = 5.

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 16 Conclusion Tackle several problems about PBC  Sensitivity to initialization.  Sensitivity to input sequence ordering  The adverse influence from outliers. Experimental results have shown the superior performance of the proposed method over several existing PBC algorithms. MDL framework can tackle the problem of compact clusters and sparse clusters simultaneously existed.

17. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 17 Personal Comments Advantage  A heuristic way to tackle outlier problem. Drawback  Application  clustering  classification