The 8th ICDM Workshop on High Dimensional Data Mining
In conjunction with the IEEE International Conference on Data Mining (IEEE ICDM 2020)
November 17-20, 2020,
Sorrento, Italy à Virtual
DM1140 - Accelerated
SGD for Tensor Decomposition of Sparse Count Data
Huan He, Yuanzhe Xi, Joyce Ho
DM454 - Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering
Daniyal Kazempour, Peer Kröger, Thomas Seidl
DM933 - Individualized Context-Aware Tensor Factorization for Online Games Predictions
Julie Jiang, Kristina Lerman, Emilio Ferrara
S06201 - You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters
Daniyal Kazempour, Long Matthias Yan, Peer Kröger and Thomas Seidl
S06202 - I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering
Anna Beer, Daniyal Kazempour, Peer Kröger, Thomas Seidl
S25201 - Efficient distance-based global sensitivity analysis for terrestrial ecosystem modeling
Description of Workshop
Over a decade ago, 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.
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.
For a number of reasons, 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.
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.
This workshop aims to promote new advances and research directions to address the curses, as well as to uncover and exploit the blessings of high dimensionality in data mining.
This year we would like to particularly encourage submissions that define and exploit some notion of "intrinsic dimension" or more generic "intrinsic structure" in learning and/or optimisation problems that allows solving high dimensional data mining tasks more reliably and more efficiently.
Topics of interest include (but are not limited to) the following:
- What are some useful notions of intrinsic structure for high dimensional data mining?
- How to devise data mining algorithms that scale with a suitable notion of intrinsic structure?
- Plausible models of low intrinsic structure, such as sparse representation, manifold models, latent space models, and studies of their noise tolerance.
- 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 data mining where the data dimension is larger than the sample size.
- Adaptive and non-adaptive dimensionality reduction for high dimensional data sets.
- Random projections, and random matrix theory applied to high dimensional data mining.
- Classification, regression, clustering, and visualisation of high dimensional complex data sets.
- Functional data mining.
- Data mining applications to real problems in science, engineering or businesses where the data is high dimensional.
High quality original submissions are solicited for oral and poster presentation at the workshop. The page limit of workshop papers is 8 pages in the standard IEEE 2-column format (https://www.ieee.org/conferences/publishing/templates.html), including the bibliography and any possible appendices. Reviewing is triple-blind! Therefore, please do not include author identifying information.
All papers must be formatted according to the IEEE Computer Society proceedings manuscript style, following IEEE ICDM 2020 submission guidelines, which are the same as for the main conference (except the page limit). All accepted workshop papers will be published in the IEEE Computer Society Digital Library (CSDL) and IEEE Xplore, and indexed by EI.
· Submission deadline: 24 August 2020. Extended to 2 September.
· Workshop paper notifications: September 17, 2020
Registration & Expenses
Every workshop paper must have at least one full paid conference registration in order to be published. Check the main conference pages for details.
Workshop organisation & contact
School of Computer Science, University of Birmingham, UK
Related Links & resources: Analytics, Big Data, Data Mining, & Data Science Resources