The 6th ICDM Workshop on High Dimensional Data Mining
In conjunction with the IEEE International Conference on Data Mining (IEEE ICDM 2018)
November 17, 2018.
– 1:50: Mihir Shekhar, Lini Thomas, and Kamalakar Karlapalem,
High Dimensional Clustering: A Strongly Connected Component Clustering Solution (SCCC)
1:50 – 2:10: Bishal Deb, Ankita Sarkar, Nupur Kumari, Akash Rupela,
Piyush Gupta, and Balaji
Multimapper: Data Density Sensitive Topological Visualization
– 2:30: Mohammed Wasid and Rashid Ali,
Clustering Approach for Multidimensional Recommender Systems
– 2:50: Donglin Wang,
HILLS: Hierarchical Indoor Localization for Large-Scale Architectural Complex
– 3:10: Ryan Moulton and Yunjiang Jiang,
Maximally Consistent Sampling and the Jaccard Index of Probability Distributions
3:10-3:30 coffee break
– 3:50: Hyunmin Lee, Zhen Hao
Wu, and Zhaolei Zhang,
Dimension Reduction on Open Data using Variational Autoencoder
– 4:10: Ao Yin and Chunkai
BOFE: Anomaly Detection in Linear Time Based On Feature Estimation
– 4:30: Yingjing Lu,
Mining Connections between Domains through Latent Space Mapping
– 4:50: Qizhi Zhang, Kuang-Chih
Lee, Hongying Bao, Yuan
You, Wenjie Li, and Dongbai
Large scale classification in deep neural network with Label Mapping
– 5:10: Youssef Hmamouche, Lotfi
Lakhal, and Alain Casali,
Predictors Extraction in Time Series using Authorities-Hubs Ranking
– 5:30: Arthur Flexer, Monika Dörfler,
Jan Schlüter, and Thomas Grill,
Hubness as a case of technical algorithmic bias in music recommendation
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 and 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 "intrinsic structure" in the learning or optimisation problem that allows solving high dimensional data mining tasks more reliably and more efficiently.
Topics of interest include all aspects of high dimensional data mining, including 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 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 2018 submission guidelines, which are the same as for the main conference (except the page limit), and available at http://icdm2018.org/calls/call-for-papers/. Accepted papers will be included in the IEEE ICDM 2018 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. The workshop proceedings will be in a CD separated from the CD of the main conference. The CD is produced by IEEE Conference Publishing Services (CPS).
· Submission deadline: 7th August, 2018. SUBMIT HERE
· Workshop paper notifications: September 4, 2018
· Camera-ready deadline for the final version of accepted papers: September 15, 2018
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.
Michael E. Houle
Workshop organisation & contact
School of Computer Science, University of Birmingham, UK
Related Links & resources: Analytics, Big Data, Data Mining, & Data Science Resources