* 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

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 
*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
-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.

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)

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

        Contacts: email to [log in to unmask]

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