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Using Interactive Evolution for Exploratory Data Analysis
Tomáš ŘehořekCzech 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
DataMining
Evolutionarycomputation
ArtificialIntelligence
Optimization
Machinelearning
ArtificialNeural Networks
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
PicBreeder
Jimmy Secretan
Kenneth Stanley
Interactive Evolution
by
Next
generation
…
and so on
…
And after 75 generations ...
... you eventually get something interesting
The technology hidden behind
x
z
grayscale
x
z
Neural net draws the image
Neuroevolution
grayscale
By clicking, you increase fitness of nets
Next generations inherit fit building patterns
x
z
Gallery of discovered images
Using Interactive Evolutionin Exploratory Data Analysis Experiment with evolving
projections : nf 2
Examples inn-dimensional
space
2D
Interactive Evolution of Projections
Machine
Human
Candidateprojections
FeedbackFeedback
Interactive Evolution of Projections
Machine
Human
Candidateprojections
Feedback
Feedback
Data Projection Experiments
Linear transformation Evolve coefficient matrix
Do the transformation using formula:
… resulting a point in 2D-space
1 2 n
1 2 n
, , ,
, , ,
a a a
b b b
f a x b xxn n
i i i ii=1 i=1,
Data Projection Experiments Sigmoidal transformation
Evolve coefficient matrix
Do the transformation using formula:
a a a b b b c c c
a a a b b b c c c1,1 1,2 1,n 1,1 1,2 1,n 1,1 1,2 1,n
2,1 2,2 2,n 2,1 2,2 2,n 2,1 2,2 2,n
, , , , , , , , , , ,
, , , , , , , , , , ,
+ +
b x c b
a af x
1,i i 1,i 2,i i 2,i
n n1,i 2,i
x ci=1 i=11 e 1 e,
a
b
c
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 Řehoř[email protected]
Computational Intelligence Group (CIG)
Faculty of Information Technology (FIT)
Czech Technical University (CTU) in Prague