1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 The Evolving Tree — Analysis and Applications Advisor : Dr. Hsu Presenter : Zih-Hui Lin Author :Jussi Pakkanen, Jukka Iivarinen, and Erkki Oja, IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 3, MAY 2006

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2  Motivation  Objective  ETree  ETree-Analysis  Experiments  Conclusions Outline

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation  Some of its intrinsic features make it unsuitable for analyzing very large scale problems.  This converts complexity control from a global problem to a local one which is simpler.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective  We have analyzed and compared the ETree against many different systems.

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 ETree BMU Training data XiXi 1)Find the BMU using the search tree 2) Update the leaf node locations using the SOM training formulas substituting tree distance for grid distance. hop 3) Increment the BMU’s hit counter. 4) If the counter reaches the splitting threshold, split the node.

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 ETree- How to controlling the growth

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 ETree - Removing Layers  One beneficial feature of most neural networks is graceful degradation.

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 ETree - better Search for the BMU  At every layer we keep the n best subbranches instead of only one. 51 52 55 Regular BMU

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 ETree - Child Node Initialization  The first perturbs the child nodes randomly.  The second one is based on principal component analysis (PCA).

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 ETree - Optimizing the Leaf Node Locations 1)First, we map all training vectors to leaf nodes using the established BMU search. 2)Then we move the leaf nodes to the center of mass of their respective data vectors. Large dataset

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Visualization experiments data vector ETree Leaf nodes SOM K-means

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Quality of clustering without the search tree

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 13 Conclusions  ETree’s performance is quite close to classical, nonhierarchical algorithms but it is noticeably faster,  ETree makes implementation and application easier.

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 14 Advantage: … Disadvantage: … Apply clustering, classification, large dataset My opinion