Using Interactive Evolution
for Exploratory Data Analysis
Tomáš Řehořek
Czech Technical University in Prague
CIG Research Group

Czech Technical University in Prague

Faculty of Electrical Engineering (FEL)
 Faculty of Information Technology (FIT)
CIG Research Group

Data Mining


Algorithms, Visualization, Automation
Biologically inspired algorithms

Evolutionary computation
 Artificial neural networks

Artificial Intelligence

Machine learning, Optimization
Optimization in Data Mining

Main objective of the CIG research group
Evolutionary
computation
Data
Mining
Artificial
Neural Networks
Machine
learning
Artificial
Intelligence
Optimization
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
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.org
Interactive
Evolution
PicBreeder
by
Jimmy
Secretan
Kenneth
Stanley
Next
generation
…
and so on
…
And after 75 generations ...
... you eventually get something interesting
The technology hidden behind
x
z
grayscale
z
Neural net draws the image
x
Neuroevolution
x
grayscale
z
By clicking, you increase fitness of nets
Next generations inherit fit building patterns
Gallery of discovered images
Using Interactive Evolution
in Exploratory Data Analysis

Experiment with evolving
projections
f:
n

Examples in
n-dimensional
space
2D
2
Interactive Evolution of Projections
Machine
Candidate
projections
   



Feedback
Feedback
Human
Interactive Evolution of Projections
Machine
Candidate
projections





Feedback
Feedback
Human
Data Projection Experiments

Linear transformation
 Evolve
coefficient matrix
a1, a2 ,
b , b ,
 1
2
 Do
, an 
, bn 
the transformation using formula:
f  x   i=1ai  xi,

n

… resulting a point in 2D-space
n
i=1
bi  xi 

Data Projection Experiments

Sigmoidal transformation
 Evolve
 a1,1,
a ,
 2,1
coefficient matrix
a1,2 ,
,
a1,n ,
b1,1,
b1,2 ,
,
b1,n ,
c1,1,
c1,2 ,
,
a2,2 ,
,
a2,n ,
b2,1,
b2,2 ,
,
b2,n ,
c2,1,
c2,2 ,
,
 Do
the transformation using formula:
a1,i
a2,i
 n

n
f  x   i=1
, i=1
b1,i  xi c1,i 
b2,i  xi c 2,i  
1+ e
1+ e


b
a
c
c1,n 
c2,n 
Experiments with Wine Dataset
PCA
SOM
Separation of Different Classes
using Linear Projection
Separation of Different Classes
using Sigmoidal Projection
There are many possible goals!
„Blue points down“ – 5 generations, sigmoid
projection
Outlier Detection – 8 generations, linear projection
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

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