Biography
Michel van de Velden is an associate Professor of Statistics at the Econometric Institute of the Erasmus University Rotterdam. His main research interests are exploratory data analysis. In particular, dimension reduction and cluster analysis methods with a strong focus on data visualization. In addition, he is in involved in several supervised machine learning projects involving tree-based machine learning methods.
More information
Work
- Michel van de Velden, A Iodice D'Enza & F Palumbo (2025) - cluster correspondence analysis - Psychometrika, 82 (1), 158-185 - doi: 10.1007/s11336-016-9514-0 - [link]
- Rick Willemsen, Carlo Cavicchia, Wilco van den Heuvel & Michel van de Velden (2025) - An Exact Solution Approach for Hierarchical Clustering - [link]
- Rick Willemsen, Carlo Cavicchia, Wilco van den Heuvel & Michel van de Velden (2025) - An Exact Solution Approach for Hierarchical Clustering - INFORMS Journal on Computing - doi: 10.1287/ijoc.2024.0903
- Alfonso Iodice D’Enza, Angelos Markos, Michel van de Velden & Carlo Cavicchia (2025) - manydist: Unbiased Distances for Mixed-Type Data - [link]
- Michel van de Velden, Alfonso Iodice D’Enza, Angelos Markos & Carlo Cavicchia (2024) - Unbiased mixed variables distance - [link]
- Sabine Knapp & Michel van de Velden (2024) - Predicting inspection outcomes and evaluating port state control targeting using random forests - doi: 10.13140/RG.2.2.26683.43040
- Rick S.H. Willemsen, Michel van de Velden & Wilco van den Heuvel (2024) - On the uniqueness of correspondence analysis solutions - Linear Algebra and Its Applications, 690, 162-185 - doi: 10.1016/j.laa.2024.03.014 - [link]
- Michel van de Velden, Alfonso Iodice D’Enza, Angelos Markos & Carlo Cavicchia (2024) - A general framework for implementing distances for categorical variables - Pattern Recognition, 153 - doi: 10.1016/j.patcog.2024.110547 - [link]
- Sabine Knapp & Michel van de Velden (2024) - Improved risk predictions of vessels using machine learning: how effective is the status quo?
- Rick S.H. Willemsen, Wilco van den Heuvel & Michel van de Velden (2023) - A new mixed integer programming approach for inverse correspondence analysis - Computers and Operations Research, 160 - doi: 10.1016/j.cor.2023.106375 - [link]
EQI
- Start date approval
- juli 2025
- End date approval
- juli 2028
- Place
- ROTTERDAM
Applied Statistics 1
- Year
- 2024
- Course Code
- FEB11005
Applied Statistics 1
- Year
- 2024
- Course Code
- FEB11005X
Seminar Data Science for Marketing Analy
- Year
- 2024
- Course Code
- FEM11152
