Intelligent Database Systems Lab Presenter : Chuang, Kai-Ting Authors : Rodrigo T. Peres, Claus...

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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

Outlines Motivation Objectives Methodology Experiments Conclusions Comments

Motivation• The problem of data visualization consists of

generating a bi-dimensional projection of a high-

dimensional data set.

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.

Methodology-Framework

Methodology

Methodology-Cauchy-Schwartz divergence measure

Methodology-Information Theoretic Learning (ITL)

Methodology-Computational complexity of the Dcs

Methodology-Differential Evolution

Methodology-Data transformation

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 .

Experiment-Kernel width

Experiment-Synthetic data sets1

Experiment-Synthetic data sets2

Experiment-Real world data sets

Conclusions• Using this method, we promote the bi-dimensional

visualization of high-dimensional data sets with

optimized cluster separation.

Comments• Advantages– The method performed well .• Disadvantages– It may be slower to train on data sets with a larger

number of cases. • Applications– Visualization.

Intelligent Database Systems Lab Presenter : Chuang, Kai-Ting Authors : Rodrigo T. Peres, Claus...

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