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The first international Workshop on topological learning
In conjunction with ISMIS 2009
http://eric.univ-lyon2.fr/~topolearn/
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CALL FOR PAPER

There is a growing interest in learning data topology,
from theoretical results to real-world applications.
Topological learning is an emerging field which is expected to
bring new insights in all the areas of the data
mining and knowledge discovery process.

Data mining and knowledge discovery (KDD),
as stated in their early definition, can today
be considered as stable fields with numerous efficient
methods and studies that have been proposed to extract
knowledge from data. In every data mining task,
understanding the structure of multidimensional patterns,
in supervised or unsupervised cases, is of fundamental importance.
Topological learning aims at finding hidden structures (usually low-dimensional manifolds)
in order to better understand and exploit data.

The aim of this workshop is to address
issues related to the concepts of learning
data topology. Our goal is to attract papers dealing with
each step of this field. Actually, learning data topology
within the KDD process implies to work on every step,
starting from the pre-processing (e.g. structuring and
organizing) to the visualization and interpretation of
the results, via the data mining methods themselves.

There are still big practical and
theoretical challenges dealing with data topology, among
which: how to deal with noisy, high-dimensional,
multi-scale or complex data (times series, images,
graphs, trees, texts, etc.)? How to interpret and qualify the results?
How to select the models complexity? How to design efficient algorithms with theoretical
guarantees? When is topological learning beneficial against usual methods? Etc. Papers dealing with these
issues and reporting applications on real data will be of fundamental interest for
this emerging field, and will take high priority for the selection process.
KDD fields which involve topological learning, include, but are not limited to:

- Topology learning
- Manifold learning
- Spectral clustering and embedding
- Spectral feature selection
- Linear and non linear dimensionality reduction
- Computational topology
- Mathematical morphology
- Geometric Inference
- Topological learning and complex data
- Applications and experience feedback

More and more people approach the field of topological learning from different and
interesting angles. They come from various communities such as data mining, statistics,
geometry, topology, physics, medicine or engineering. We believe that now is the
right time to establish and enhance cross fertilization between these communities.
During this workshop, researchers will have the opportunity to bring new ideas,
discuss their experiences and contribute to the theoretical and practical maturation of topological learning.

The workshop will consist in a series of communications (oral presentations or poster).
A reasonable time will be left for the discussion after each presentation.
All the articles will be reviewed at least twice with a goal to
improve their quality and give advice to the authors. A dedicated place will
be given to the young researchers with a session (Position paper) grouping
the work in progress in the various European teams. That can be the occasion
for a PhD student or a young researcher to present his/her starting project.
This session will be particularly significant for work on the beginning and the
installation of research groups on shared topics. Demonstrations of research
results could be associated with the poster presentations.

INTRUCTIONS FOR AUTHORS

The submissions should be written in English and should not exceed 10 pages in the Springer Verlag format.

Submitted papers will be evaluated by at least three reviewers.
Any submission that exceeds length limits or
deviates from formatting requirements may be rejected without review.

IMPORTANT DATES

Abstract submission June 1, 2009
Paper submission June 7, 2009
Notifications July 12, 2009
Camera-ready version July 31, 2009
Workshop September 14, 2009

PROGRAM CHAIRS

- Micha?l AUPETIT, CEA-DAM, France
- Hakim HACID, Alcatel-Lucent Bell Labs, France
- Djamel A. ZIGHED, University of Lyon 2, France

PROGRAM COMMITTEE (to be completed..)
- Micha?l AUPETIT, CEA-DAM, France
- Youn?s BENNANI, Univ. Paris 13, Paris, France
- Stephane BONNEVAY, Univ. Lyon 1, France
- Marc BUI, EPHE, Paris
- Fr?d?ric CHAZAL, INRIA, Orsay, France
- David COHEN-STEINER, INRIA, Orsay, France
- Marie COTRELL, Univ. Dauphine, Paris, France
- Pierre GAILLARD, CEA, DAM, Arpajon, France
- Hakim HACID, Alcatel-Lucent Bell Labs, France
- Michel LAMURE, Univ. Lyon 1, France
- John LEE, Belgium
- Sylvain LESPINATS, CEA, LIST, Saclay, France
- Thomas MARTINETZ, Univ. of Luebeck, Germany
- Mikhail BELKIN, The Ohio State University, USA
- Steve OUDOT, INRIA, Orsay, France
- Fabrice ROSSI, ENST, Paris, France
- Vin de SILVA, Pomona College, California, USA
- Michel VERLEYSEN, UCL, Louvain-la-Neuve, Belgium