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 Programme
Overview
Detailed
Programme
| Clustering
and Unsupervised Learning (CUL) |
CHAIR: TATIANA TAMBOURATZIS
Time: Wednesday, March 23rd, 11h00-12h40
| CUL-1 |
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| Title: |
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Improved clustering by rotation
of cluster centres |
| Author(s): |
D. W. Pearson,
M. Batton-Hubert |
| Abstract: |
In this paper we present a method that leads
to the improvement of a subtractive clustering model by modifying
the centres. In order to keep within certain bounds, a centre
is modified by rotating it. |
| CUL-2 |
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| Title: |
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Hierarchical Growing Neural Gas |
| Author(s): |
K.A.J. Doherty,
R.G. Adams,
N. Davey |
| Abstract: |
This paper describes TreeGNG, a top-down unsupervised
learning method that produces hierarchical classification
schemes. TreeGNG is an extension to the Growing Neural Gas
algorithm that maintains a time history of the learned topological
mapping. TreeGNG is able to correct poor decisions made during
the early phases of the construction of the tree, and provides
the novel ability to influence the general shape and form
of the learned hierarchy. |
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| CUL-3 |
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| Title: |
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A Fuzzy Clustering Algorithm using
Cellular Learning Automata based Evolutionary Algorithm |
| Author(s): |
R. Rastegar,
A. Hariri,
M. Meybodi |
| Abstract: |
In this paper, a new fuzzy clustering algorithm
that uses cellular learning automata based evolutionary computing
(CLA-EC) is proposed. The CLA-EC is a model obtained by combining
the concepts of cellular learning automata and evolutionary
algorithms. The CLA-EC is used to search for cluster centers
in such a way that minimizes the clustering criterion. The
simulation results indicate that the proposed algorithm produces
clusters with acceptable quality with respect to clustering
criterion and provides a performance that is superior to that
of the C-means algorithm. |
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| CUL-4 |
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| Title: |
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Estimating the number of clusters
from distributional results of partitioning a given data set |
| Author(s): |
U. Möller |
| Abstract: |
When estimating the optimal value of the number
of clusters, C, of a given data set, one typically uses, for
each candidate value of C, a single (final) result of the
clustering algorithm. If distributional data of size T are
used, these data come from T data sets obtained, e.g., by
a bootstrapping technique. Here a new approach is introduced
that utilizes distributional data generated by clustering
the original data T times in the framework of cost function
optimization and cluster validity indices. Results of this
method are reported for model data (100 realizations) and
gene expression data. The probability of correctly estimating
the number of clusters was often higher compared to recently
published results of several classical methods and a new statistical
approach (Clest). |
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| CUL-5 |
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| Title: |
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AUDyC Neural Network using a new
Gaussian Densities Merge Mechanism |
| Author(s): |
Habiboulaye Amadou
Boubacar,
Stéphane Lecoeuche,
Salah Maouche |
| Abstract: |
In the context of evolutionary data classification,
dynamical modeling techniques are useful to continuously learn
clusters models. Dedicated to on-line clustering, the AUDyC
(Auto-adaptive and Dynamical Clustering) algorithm is an unsupervised
neural network with auto-adaptive abilities in non-stationary
environment. These particular abilities are based on specific
learning rules that are developed into three stages: “Classification”,
“Evaluation” and “Fusion”. In this
paper, we propose a new densities merge mechanism to improve
the “Fusion” stage in order to avoid some local
optima drawbacks of Gaussian fitting. The novelty of our approach
is to use an ambiguity rule of fuzzy modelling with new merge
acceptance criteria. Our approach can be generalized to any
type of fuzzy classification method using Gaussian models.
Some experiments are presented to show the efficiency of our
approach to circumvent to AUDyC NN local optima problems. |
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