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 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