1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Fast exact k nearest neighbors search using an orthogonal search tree Presenter : Chun-Ping Wu Authors :Yi-Ching Liaw, Maw-Lin Leou, Chien-Min Wu PR 2010 國立雲林科技大學 National Yunlin University of Science and Technology 1

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 finding process of k nearest neighbors for a query point using FSA(full search algorithm) is very time consuming. Many algorithms want to reduce the computational complexity of the kNN finding process.  Pre-created tree structure 3

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation For a big PAT(Principal Axis Search), the computation time to evaluate boundary points and projection values will be large. 4

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To reduce the computation time on evaluation boundary points and projection values in the kNN searching process for a query point. The proposed method requires no boundary points and only little computation time on evaluating projection values in the kNN finding process. 5

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology The OST(orthogonal search tree) algorithm  OST construction process  K Nearest neighbors search using the OST 6

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology The OST construction process 7 1,2,3, 4,5,6, 7,8,9 1,2,3, 4,5,6, 7,8,9 1,2,34,5,67,8,9 1,2,3, 4,5,6, 7,8,9 1,2,34,5,67,8,9 123

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology K nearest neighbors search using the orthogonal search tree 8 1,2,3, 4,5,6, 7,8,9 1,2,34,5,67,8,9 123 456789

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Example 1  Uniform Markov source 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Example 2  auto-correlated data 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Example 3  Clustered Gaussian data 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Example 4  Data sets are codebook generated using 6 real images. 13

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Example 5  Statlog data set. 14 34%39%

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 15 Experimental results show that the proposed method always spends less computation time to find the kNN for a query point than the other methods. The proposed method will find the same results as those of the FSA(full search algorithm).

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments 16 Advantage  To reduce the computation of the kNN finding process. Drawback  Lack of illustrations Application  Classification