[apologies for cross-posting]
The 6th International Workshop on High Dimensional Data Mining (HDM
2018)
In conjunction with the IEEE International Conference on Data Mining
(IEEE ICDM 2018)
NOVEMBER 17-20, 2018, SINGAPORE.
http://www.cs.bham.ac.uk/~axk/HDM18.htm
** SUBMISSION DEADLINE: AUGUST 7, 2018 **
Call For Papers
This workshop aims to promote new advances and research directions to
address the curses, and to uncover and exploit the blessings of high
dimensionality in data mining.
Topics of interest include the following:
- Systematic studies of how the curse of dimensionality affects data
mining methods
- Models of low intrinsic dimension: sparse representation, manifold
models, latent structure models, large margin, other?
- How to exploit intrinsic dimension in optimisation tasks for data
mining?
- New data mining techniques that scale with the intrinsic dimension, or
exploit some properties of high dimensional data spaces
- Dimensionality reduction
- Methods of random projections, compressed sensing, and random matrix
theory applied to high dimensional data mining and high dimensional
optimisation
- Theoretical underpinning of mining data whose dimensionality is larger
than the sample size
- Classification, regression, clustering, visualisation of high
dimensional complex data sets
- Functional data mining
- Data presentation and visualisation methods for very high dimensional
data sets
- Data mining applications to real problems in science, engineering or
businesses where the data is high dimensional
High quality original submissions are solicited. Papers should not
exceed 8 pages, and should follow the IEEE ICDM format requirements of
the main conference. All submissions will be peer-reviewed, and the
accepted papers will be published in the proceedings by the IEEE
Computer Society Press.
Submission deadline: August 7-th, 2018 at 23:59 Pacific Standard Time
Notifications to authors: September September 4-th, 2018
For more information see:
http://www.cs.bham.ac.uk/~axk/HDM18.htm
--
Ata Kaban
School of Computer Science
University of Birmingham
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