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April 2012

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Subject:
From:
Francesco Masulli <[log in to unmask]>
Reply To:
Classification, clustering, and phylogeny estimation
Date:
Wed, 4 Apr 2012 18:50:14 +0200
Content-Type:
Text/Plain
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Text/Plain (109 lines)
* Our apologies if you receive multiple copies of this announcement *

Please note:
- Abstract submission deadline: 25 April 2012
- A selection of papers will be published in extend form in a post-conference
volume in the series LNCS of Springer

*******************************************************************************
                         International Workshop on

                    Clustering High-Dimensional Data

Palazzo Serra di Cassano, Via Monte di Dio 14, Naples (Italy), May 15th, 2012
           http://sites.google.com/site/chdd12naples/


One of the strongest problems afflicting current machine learning techniques is
dataset dimensionality. In many applications to real world problems, we deal
with data with anywhere from a few dozen to many thousands of dimensions. Such
high-dimensional data spaces are often encountered in areas such as medicine
or biology, where DNA microarray technology can produce a large number of
measurements at once, the clustering of text documents, where, if a word-
frequency vector is used, the number of dimensions equals the size of the
dictionary, and many others, including data integration and management, and
social network analysis. In all these cases, the dimensionality of data makes
learning problems hardly tractable.

In particular, the high dimensionality of data is a highly critical factor for
the clustering task. The following problems need to be faced for clustering
high-dimensional data:
*When the dimensionality is high, the volume of the space increases so fast
that the available data becomes sparse, and we cannot find reliable clusters,
as clusters are data aggregations (curse of dimensionality).
*The concept of distance becomes less precise as the number of dimensions
grows, since the distance between any two points in a given dataset converges
(concentration effects).
*Different clusters might be found in different subspaces, so a global filtering
of attributes is not sufficient (local feature relevance problem).
*Given a large number of attributes, it is likely that some attributes are
correlated. Hence, clusters might exist in arbitrarily oriented affine
subspaces.
*High-dimensional data could likely include irrelevant features, which may
obscure the effect of the relevant ones.

The workshop is aimed to the study of current approaches towards clustering
high-dimensional data and its topic areas include, but are not limited to,
approaches based on
-relational clustering
-data reduction using rough and fuzzy sets
-subspace clustering
-projected clustering
-correlation clustering
-biclustering/co-clustering
-clustering ensembles
-multi-view clustering
and to related methods, such as those for intrinsic dimension estimation and
for clustering comparison.

We solicit original or survey contributions (including work in progress) that
contribute to this research area in data clustering. Participants who wish to
give a talk should submit an extended abstract (max. 2 pages in free format
including paper title, authors and affiliations) before April 25th, 2012 through
the Easy Chair website http://www.easychair.org/conferences/?conf=chdd12

Abstracts will be referred as soon they will be submitted and the decision
will be sent to authors.

Authors of presented papers will be invited to submit a full paper for a post-
workshop volume to be published in the Springer's LNCS series.

There are no registration fees, but for organization purpose we request that
participants register form ASAP and possibly before April 30th, 2012 con the
workshop web site.

Deadlines:
April 25th, 2012 Abstract submission (max 2 pages as pdf file)
April 30th, 2012 Abstract acceptation
April 30th, 2012 Registration (no fees, but required)
May 15th, 2012 Workshop
Jul 20th, 2012 Full papers submission

Workshop Organizers:
Francesco Masulli - University of Genova, Genoa (Italy)
Alfredo Petrosino - University of Napoli Parthenope, Naples (Italy)

Sponsors:
GNCS Gruppo Italiano di Calcolo Scientifico
IISF Istituto Italiano per gli Studi Filosofici
SIGBI Special Interest Group in Bioinformatics and Intelligence of INNS
TFNN Task Force in Neural Networks of IEEE-CIS-TCBB
DISI Dept. Computer and Information Science - Univ. Genova, Italy
DSA Dept. Applied Science, Univ. Naples Parthenope

*******************************************************************************
                         International Workshop on

                    Clustering High-Dimensional Data

Palazzo Serra di Cassano, Via Monte di Dio 14, Naples (Italy), May 15th, 2012
           http://sites.google.com/site/chdd12naples/

        Contacts: email to [log in to unmask]
*******************************************************************************
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