[Liste-proml] ICONIP'12 Special Session on Co-clustering of Large and High Dimensional Data: call for papers

Nistor Grozavu Nistor.Grozavu at lipn.univ-paris13.fr
Jeu 3 Mai 10:58:33 CEST 2012


Dear Colleagues,
2012 International Conference on Neural Information Processing (ICONIP 
2012) will be held in Doha, Qatar. It is an annual event, organized 
since 1994 by the Asia Pacific Neural Network Assembly (APNNA).
Submission deadline is June 1, 2012 on the ICONIP 2012 webpage: 
http://www.iconip2012.org/submission.htm

CALL FOR PAPERS :
Special Session : Co-clustering of Large and High Dimensional Data
http://www.iconip2012.org/s04.pdf

Organizers:
Younès Bennani, Full Professor, Paris 13 University.
Nistor Grozavu, Associate Professor, Paris 13 University.
Mohamed Nadif, Full Professor, Paris 5 University.
Nicoleta Rogovschi, Associate Professor, Paris 5 University.

Contact:
Nistor Grozavu, Nicoleta Rogovschi
Email: Nistor.Grozavu at lipn.univ-paris13.fr, 
nicoleta.rogovschi at parisdescartes.fr

Description:
  One of the strongest problems afflicting current machine learning 
techniques is dataset dimensionality. During the recent years, 
companies, collaborations, and organizations are increasingly faced with 
the need to analyze of large and growing collections of data. Big data 
requires technologies to efficiently process large quantities of data 
within tolerable elapsed times. The used technologies include massively 
parallel processing datasets, multi-sourcing, cloud computing platforms, 
distributed databases...

Beyond the purely quantitative, these data are presented in such a way 
that they are hardly supported by traditional machine learning methods: 
data is not organized in the form of tables and their structures can 
vary; is produced in real time; arrive worldwide in continuous streams; 
is heterogeneous and located on different sources (mobile phones, 
sensors, connected TVs, tablets, desktop PCs, laptops, objects, machines).

In particular, the high dimensionality of data is a highly critical 
factor for the clustering task. Another important challenge in 
clustering is the dimensionality reduction which deals with the 
transformation of a high dimensional data set into a low dimensional 
space, while retaining most of the useful structure in the original 
data; retaining only relevant features and observations. Different 
clusters might be found in different subspaces, so a global filtering of 
attributes is not sufficient. Although the majority of clustering 
procedures aim to construct an optimal partition of observations or, 
there are other methods, known as co-clustering in document clustering, 
biclustering in bioinformatics context or latent block models, which 
consider the two sets simultaneously and organize the data into 
homogeneous blocks. Compared to conventional clustering methods, they 
have proved their effectiveness in discovering structures from matrices 
of large high dimensional datasets.

  All these challenges can be treated together in order to establish a 
scalable, affordable, and flexible large-scale analytics infrastructure.

  The aim of this Special Session is to provide a forum for discussions 
of recent advances and future directions in the Co-clustering of Big 
Data and its topic areas include, but are not limited to:

  - subspace clustering
  - high dimensionality reduction
  - latent block models
  - model selection
  - multi-view co-clustering
  - collaborative co-clustering
  - distributed co-clustering
  - multi source co-clustering
  - consensus co-clustering
  - Nonnegative Matrix Factorization
  - incremental co-clustering
  - visualization
  - high sparse data co-clustering

Yours sincerely,
Special Session Organizers


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