1. Intelligent Database Systems Lab Presenter : Chuang, Kai-Ting Authors : Rodrigo T. Peres, Claus Aranha, Carlos E. Pedreira 2013, InfSci Optimized bi-dimensional data projection for clustering visualization

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab Motivation The problem of data visualization consists of generating a bi-dimensional projection of a high- dimensional data set.

4. Intelligent Database Systems Lab Objectives We propose a new method to project n-dimensional data onto two dimensions, for visualization purposes. We apply Differential Evolution as a meta-heuristic to optimize a divergence measure of the projected data. This divergence measure is based on the Cauchy– Schwartz divergence, extended for multiple classes.

5. Intelligent Database Systems Lab Methodology-Framework Cauchy-Schwartz divergence measure Differential Evolution Data transformation

6. Intelligent Database Systems Lab Methodology

7. Intelligent Database Systems Lab Methodology-Cauchy-Schwartz divergence measure

8. Intelligent Database Systems Lab Methodology-Information Theoretic Learning (ITL)

9. Intelligent Database Systems Lab Methodology-Information Theoretic Learning (ITL)

10. Intelligent Database Systems Lab Methodology-Information Theoretic Learning (ITL)

11. Intelligent Database Systems Lab Methodology-Computational complexity of the Dcs

12. Intelligent Database Systems Lab Methodology-Differential Evolution

13. Intelligent Database Systems Lab Methodology-Data transformation

14. Intelligent Database Systems Lab Experiment setup Synthetic data sets – Initial conditions. – Robustness of the method to very noisy dimesions. Real world data sets – Pen Digits – Lung Cancer – Compares monocytes-related dendritic cells, plasmocytoid dendritic cells and B-lymphocytes. – Compares monocytes and neutrophils. – Compares plasmocytoid dendritic cells and neutrophils.

15. Intelligent Database Systems Lab Experiment-Kernel width

16. Intelligent Database Systems Lab Experiment-Synthetic data sets1

17. Intelligent Database Systems Lab Experiment-Synthetic data sets2

18. Intelligent Database Systems Lab Experiment-Real world data sets

19. Intelligent Database Systems Lab Conclusions Using this method, we promote the bi-dimensional visualization of high-dimensional data sets with optimized cluster separation.

20. Intelligent Database Systems Lab Comments Advantages – The method performed well. Disadvantages – It may be slower to train on data sets with a larger number of cases. Applications – Visualization.