HDM'26: 13th ICDM Workshop on High Dimensional Data Mining

12 November 2026 · Shenyang, China

The 13th edition of our High Dimensional Data Mining workshop takes place as a satellite workshop of the IEEE International Conference on Data Mining (ICDM 2026, 12–15 November 2026).

This year the workshop particularly encourages work on interpretable high dimensional data mining, as well as work that counters the issues of low sample size and takes advantage of unlabelled or auxiliary data. Topics include weak supervision, prototype-based and explainable dimensionality reduction, random projections and random matrix theory, models of low intrinsic structure, and applications where the data is high dimensional.

Full details, programme and organisers: https://www.cs.science.cmu.ac.th/staff/jakramate/hdm26.html

Programme (room: Kuala Lumpur)

Each talk is 20 minutes plus 5 minutes for questions.

Time	Session
08:30	Opening remarks
08:35	Mining Compact Information Granules from High-Frequency UAV Vibration Streams for Fault Severity Classification: Kiersztyn, Karczmarek, Stęgierski, Syta, Ambrożkiewicz, Romaniuk, Koszewnik, Ołdziej, Dzienis
09:00	Pseudo Real-Time Nowcasting of Korean Employment: A Four-Part Test of Sentiment Signals with Sector-Scoped Evidence: Choo, Lee
09:25	A Fast Structural Clustering Tool for Experiential Data Science Education: Liang, Chen, Yuan, Xu
09:50	Coffee break
10:10	Robust Learning under Label Noise in High-Dimensional, Small-Sample Data via Teacher–Student Consistency Regularization: Mueanmat, Bootkrajang
10:35	CHAOSMINING: Benchmarking Post-Hoc Attribution with Sparse Informative Features in High Dimensions: Shi, Liu, Liu, Kan
11:00	Discrete Morse Theory and the Piecewise Computation of Persistent Homology: Yang, Wilsey
11:25	Initialization Shifts Finite-Sample Sparse-Recovery Crossings in Interpolating Diagonal Networks: Pendharkar, Desai
11:50	Closing remarks