1. Using Interactive Evolution for Exploratory Data Analysis Tomáš Řehořek Czech Technical University in Prague

2. CIG Research Group Czech Technical University in Prague  Faculty of Electrical Engineering (FEL)  Faculty of Information Technology (FIT)

3. CIG Research Group Data Mining  Algorithms, Visualization, Automation Biologically inspired algorithms  Evolutionary computation  Artificial neural networks Artificial Intelligence  Machine learning, Optimization

4. Optimization in Data Mining Main objective of the CIG research group Data Mining Evolutionary computation Artificial Intelligence Optimization Machine learning Artificial Neural Networks

5. Dimensionality Reduction and Visualization in Data Mining Linear projections  Principal Component Analysis (PCA)  Linear Discriminant Analysis (LDA) Non-linear projections  Multidimensional Scaling (MDS)  Sammon Projection  Kernel PCA

6. Interactive Evolutionary Computation (IEC) Evolutionary Computation using human evaluation as the fitness function Currently used almost exclusively for artistic purposes  Images, Sounds, Animations… Inspiration: http://picbreeder.orghttp://picbreeder.org

7. PicBreeder Jimmy Secretan Kenneth Stanley Interactive Evolution by

8. Next generation … and so on …

9. And after 75 generations...... you eventually get something interesting

10. The technology hidden behind x z grayscale x z Neural net draws the image

11. Neuroevolution grayscale By clicking, you increase fitness of nets Next generations inherit fit building patterns x z

12. Gallery of discovered images

13. Using Interactive Evolution in Exploratory Data Analysis Experiment with evolving projections Examples in n-dimensional space 2D

14. Interactive Evolution of Projections Machine Human Candidate projections      Feedback

15. Interactive Evolution of Projections Machine Human Candidate projections      Feedback

17. Data Projection Experiments Linear transformation  Evolve coefficient matrix  Do the transformation using formula: … resulting a point in 2D-space

18. Data Projection Experiments Sigmoidal transformation  Evolve coefficient matrix  Do the transformation using formula: a b c

19. Experiments with Wine Dataset PCASOM

20. Separation of Different Classes using Linear Projection

21. Separation of Different Classes using Sigmoidal Projection

22. There are many possible goals! „Blue points down“ – 5 generations, sigmoid projection Outlier Detection – 8 generations, linear projection

23. Conclusion Interactive Evolution can be used in Exploratory Data Analysis Our experiments show that complex projections can be easily evolved In future, we plan to investigate such evolution in fields of Data Mining other than EDA

24. Thank you for your attention! Tomáš Řehořek tomas.rehorek@fit.cvut.cz Computational Intelligence Group (CIG) Faculty of Information Technology (FIT) Czech Technical University (CTU) in Prague