Cfp: The 11th ICDM Workshop on High Dimensional Data Mining (HDM'24)

[Ata Kaban]

[apologies for cross-posting]

The 11th ICDM Workshop on High Dimensional Data Mining (HDM’24)
In conjunction with the IEEE International Conference on Data Mining

https://www.cs.bham.ac.uk/~axk/hdm24.html

Submission deadline: September 10, 2024
Call For Papers

Unprecedented technological advances lead to increasingly high dimensional data sets in all areas of science, engineering and businesses. These include genomics, proteomics, biomedical imaging, signal processing, astrophysics, finance, web and market basket analysis, among many others. Propelled by the new awareness of the importance of data, practitioners from all areas maintain large repositories of high-dimensional data, albeit only some of them are tagged/labelled, most are unlabelled raw data waiting to be taken advantage of. The number of features in such data is often of the order of thousands or millions, that is much larger than the available (labelled or unlabelled) sample size.

This workshop aims to bring together researchers from data mining, databases, data science, machine learning, statistics, and related areas to cross-pollinate ideas, facilitate collaboration, and expand the breadth and reach of methods and technology to address the curses, exploit the blessings, and forge new directions in high dimensional data mining research.

This year we would like to particularly encourage work that counters the issues of low sample size and takes advantage of unlabelled or auxiliary data for high dimensional data mining. Topics of interest include (but are not limited to) the following:

- Learning and mining with weak supervision, and exploiting unlabelled data in high dimensional settings
- Managing the tradeoff between computation cost and statistical efficiency
- Models of low intrinsic structure, such as sparse representation, manifold models, latent structure models, overparameterized models, compressible models
- Effect and mitigation of noise and the curse of dimensionality in data mining methods
Theoretical underpinning of data mining where the data dimension can be larger than the sample size
- New data mining techniques that exploit properties of high dimensional data spaces
- Adaptive and non-adaptive dimensionality reduction for high dimensional data sets
- Random projections, and random matrix theory applied to high dimensional data mining
- Functional data mining
- Data mining applications to real problems in science, engineering, businesses, and the humanities, 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 ICDMW proceedings by the IEEE Computer Society Press.