1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Community self-Organizing Map and its Application to Data Extraction Presenter: Chun-Ping Wu Authors: Take Haraguchi, Haruna Matsushita and Yoshifumi Nishio IJCNN 2009 國立雲林科技大學 National Yunlin University of Science and Technology

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

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation The human-beings have some tendencies that human- beings easily gather around the leader. The conventional SOM require post-processing in clustering process. 3

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To propose a new type of SOM algorithm, which called Community SOM (CSOM) algorithm. The feature of CSOM is that neurons create some neuron- community according to their winning frequency. The CSOM can mapping and clustering via a framework. 4

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Self-Organizing Map (SOM) Community Self-Organizing Map (CSOM) 5

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Self-Organizing Map (SOM)  Input an input vector.  Find BMU.  Update the Weight vectors.  Repeat above until to the end. 6

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Community Self-Organizing Map (CSOM) 7

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments For 2-dimensional input data(1) For 2-dimensional input data(2) For Hepta data For 3-dimensional data 8

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments For 2-dimensional input data(1) 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments For 2-dimensional input data(2) 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments For Hepta data 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments For 3-dimensional input data 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 13 The important feature of CSOM is that neurons create some neuron communities according to their winning frequency. This paper has confirmed the effectiveness of CSOM in the application to the cluster extraction. The CSOM not require post-processing in clustering process.

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments 14 Advantage  The CSOM not require post-processing in clustering process. Drawback  Have a mistake in this paper. Application  Clustering