Download subspace clustering

Dimension Selective Self-Organizing Maps
With Time-Varying Structure for Subspace
and Projected Clustering
Presenter: Jheng, Jian-Jhong
Authors: Hansenclever F. Bassani, Aluizio F. R. Araujo
2015 TNNLS
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Outlines
Motivation
Objectives
Related Work
Methodology
Experiments
Conclusions
Comments
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Motivation
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Original
SOM
It was not designed
to deal with subspace
clustering.
subspace clustering
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DSSOM
The fixed topology of SOM and DSSOM
requires deep knowledge of the date to be
defined.
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SOM-TVS
However, certain adaptations to this
approach are required for dealing with
subspace clustering.
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LARFDS
SOM
Important modifications with respect to DSSOM.
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Objectives

Important modifications with respect to DSSOM.
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SUBSPACE AND PROJECTED CLUSTERING
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SUBSPACE CLUSTERING
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PROJECTED CLUSTERING
Projected clustering seeks to assign each point to a
unique cluster, but clusters may exist in different
subspaces.
The general approach is to use a special distance
function together with a regular clustering algorithm .
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Related Work

Generalized Principal Components Analysis (GPCA)

Sparse Subspace Clustering (SSC)

Adaptive Resonance Theory (ART)

Local adaptive receptive field self-organizing map for image
color segmentation (LARFSOM)
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GPCA
PCA
GPCA
GPCA is an algebraic geometric method for clustering data
not necessarily in independent linear subspaces.
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SSC
SSC is based on the idea of writing a point (x j ) as a linear or
affine combination of neighbor data points. It uses the principle
of sparsity to choose any of the remaining data points as a
possible neighbor.
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Methodology
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EXPERIMENTS
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Conclusions

The behavior of LARFDSSOM was shown to have led to a
significant improvement over DSSOM in a number of points.

It does not need to know the exact number of clusters.

Improvement entails the computational cost.

Improvement is the clustering quality.
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