HDM 2014 - 2nd International Workshop on High Dimensional Data Mining (HDM’14)

he 2nd International Workshop on High Dimensional Data Mining (HDM’14)
http://www.cs.bham.ac.uk/~axk/HDM14.htm
http://hdataskforce.wordpress.com/
In conjunction with the
IEEE International Conference on Data Mining (IEEE ICDM 2014)
http://icdm2014.sfu.ca/home.html
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Description of Workshop
Stanford statistician David Donoho predicted that the 21st century will be the century of data. "We can say with complete confidence that in the coming century, high-dimensional data analysis will be a very significant activity, and completely new methods of high-dimensional data analysis will be developed; we just don't know what they are yet." -- D. Donoho, 2000.
Beyond any doubt, unprecedented technological advances lead to increasingly high dimensional data sets in all areas of science, engineering and businesses. These include genomics and proteomics, biomedical imaging, signal processing, astrophysics, finance, web and market basket analysis, among many others. The number of features in such data is often of the order of thousands or millions - that is much larger than the available sample size.
A number of issues make classical data analysis methods inadequate, questionable, or inefficient at best when faced with high dimensional data spaces:
1. High dimensional geometry defeats our intuition rooted in low dimensional experiences, and this makes data presentation and visualisation particularly challenging.
2. Phenomena that occur in high dimensional probability spaces, such as the concentration of measure, are counter-intuitive for the data mining practitioner. For instance, distance concentration is the phenomenon that the contrast between pair-wise distances may vanish as the dimensionality increases. This makes the notion of nearest neighbour meaningless, together with a number of methods that rely on a notion of distance.
3. Bogus correlations and misleading estimates may result when trying to fit complex models for which the effective dimensionality is too large compared to the number of data points available.
4. The accumulation of noise may confound our ability to find low dimensional intrinsic structure hidden in the high dimensional data.
5. The computation cost of processing high dimensional data or carrying out optimisation over a high dimensional parameter spaces is often prohibiting.
Topics
This workshop aims to promote new advances and research directions to address the curses and uncover and exploit the blessings of high dimensionality in data mining. Topics of interest include (but are not limited to):
- Systematic studies of how the curse of dimensionality affects data mining methods
- New data mining techniques that exploit some properties of high dimensional data spaces
- Theoretical underpinning of mining data whose dimensionality is larger than the sample size
- Stability and reliability analyses for data mining in high dimensions
- Adaptive and non-adaptive dimensionality reduction for noisy high dimensional data sets
- Methods of random projections, compressed sensing, and random matrix theory applied to high dimensional data mining and high dimensional optimisation
- Models of low intrinsic dimension, such as sparse representation, manifold models, latent structure models, and studies of their noise tolerance
- Classification 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
Paper submission
High quality original submissions are solicited for oral and poster presentation at the workshop. Papers should not exceed a maximum of 8 pages, and must follow the IEEE ICDM format requirements of the main conference. All submissions will be peer-reviewed, and all accepted workshop papers will be published in the proceedings by the IEEE Computer Society Press. Submit your paper here.
Important dates
Submission deadline: August 1, 2014
Notifications to authors: September 26, 2014
Workshop date: December 14, 2014