The 9th ICDM Workshop on High Dimensional Data Mining (HDM’21)
In conjunction with the IEEE International Conference on Data Mining

7-10 December, 2021 Auckland, New Zealand

Program (7-th December 2021, 2:00-4:30pm, NZ time)


S07205: Julian Busch, Maximilian Hünemörder, Janis Held, Peer Kröger, and Thomas Seidl, "Implicit Hough Transform Neural Networks for Subspace Clustering"

DM576: Anna Beer, Lisa Stephan, and Thomas Seidl, "LUCKe- Connecting Clustering and Correlation Clustering"


S07202: Juergen Prinzbach, Tobias Lauer, and Nicolas Kiefer, "Accelerating Density-Based Subspace Clustering in High-Dimensional Data"


S07204: Yamonporn Thummanusarn and Ata Kaban, "Random Projection through the Lens of Data Complexity Indicators"


DM747: Chris Connell and Yang Wang, "TensorMode Algorithm for Network Embedding in Dynamic Environments"


S07203: Yash Garg, "ReTriM: Recontructive Triplet Loss for Learning Reduced Embeddings for Multi-Variate Time Series"


S07201: Shuo Liu and Liwen Xu, "Anomaly Detection with Dual Adversarial Training"


DM340: Jing Yang, Gaojin Fan, Anbo Shen, and Aiguo Wang, "Causal structure learning of nonlinear additive noise model based on streaming feature"


Join Zoom Meeting

Meeting ID: 985 8882 4347
Passcode: 8TnFAghh_N



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.


Paper submission

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 (, 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 2021 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.

Important dates

·         Submission deadline: 3 September 2021. Submit here

·         Workshop paper notifications: 24 September, 2021


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.


Program committee

Karim Abou-Moustafa

Jakramate Bootkrajang

Ata Kaban

Minqing Li

Momodou Sanyang

Frank-Michael Schleif

Andrew Turner


Workshop organisation & contact

Ata Kaban

School of Computer Science, University of Birmingham, UK


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