Projecten per jaar
Organisatieprofiel
Introductie / missie
The chair studies data mining (DM) techniques and knowledge discovery approaches that are at the core of data science. The group is known for its contributions to the areas of predictive analytics, automation of machine learning and networked science, subgroup discovery and exceptional model mining, and similarity computations on complex data. Its research is inspired by theoretical computer science, systems development and real-world applications of (big) data-driven discovery in healthcare, banking, energy, retail, telecom, and education among others.
Over de organisatie
We develop generic approaches and specialized techniques that cover a wide range of descriptive, predictive and prescriptive analytics and work effectively with text, image, transactional, graph and time-series data in a responsible manner. E.g. we use Deep Learning methods to develop models for high dimensional heterogeneous, unstructured and evolving data and apply this models to areas such as medical imaging, genomics, anomaly detection and sentiment analysis. We further work on methods for analyzing and explaining the model’s decisions and performance and facilitate effective DM with domain expert in the loop.
Success stories
We have created OpenML: an online collaborative platform for studying machine learning techniques. OpenML is used by almost 2,000 researchers, students, and practitioners world-wide, and contains around 20,000 datasets, 3,000 machine learning workflows, and 1,7 million shared experiments. It has won the Dutch Data Prize, as well as backing from Microsoft Research. It is crucial for the development of automated machine learning that is adopted by companies such as Philips.
Further information at OpenML.org
Project examples
- NWO RATE-Analytics (with Tilburg University, Rabobank and Achmea) "Next generation predictive analytics for data-driven banking and insurance".
- ImpulseKYC-Analytics (with Rabobank) "Know your customer predictive analytics" project aims at developing approaches for effective DM on heterogeneous and evolving data sources with expert-in-the-loop.
- STW CAPA (with Adversitement and StudyPortals)"Context-aware predictive analytics" advanced the current state of the art in Web analytics.
- NWO Veni "Detection methods for similarity structures in time-dependent data"develops foundations for advanced time series and trajectories clustering.
- H2020 SODA (ICT-2016-1; Big Data PPP) "Scalable Oblivious Data Analytics" facilitates secure DM; together with Crypto group we develop practical approaches for DM with multi-party computation.
Vingerafdruk Duik in de onderzoeksthema's waar Data Mining actief is. Deze onderwerplabels komen voort uit het werk van deze leden van de organisatie. Samen vormen ze een unieke vingerafdruk.
Learning systems
Engineering en materiaalwetenschappen
Data mining
Engineering en materiaalwetenschappen
Classifiers
Engineering en materiaalwetenschappen
Hypermedia systems
Engineering en materiaalwetenschappen
Video streaming
Engineering en materiaalwetenschappen
Students
Engineering en materiaalwetenschappen
Feedback
Engineering en materiaalwetenschappen
Experiments
Engineering en materiaalwetenschappen
Netwerk
Recente externe samenwerking op landenniveau. Duik in de details door op de stippen te klikken.
Profielen
Wouter Duivesteijn
- Faculteit Wiskunde & Informatica, Information Systems WSK&I - Universitair Docent
- Faculteit Wiskunde & Informatica, Data Mining
Persoon: UD : Universitair Docent
Vlado Menkovski
- Faculteit Wiskunde & Informatica, Information Systems WSK&I - Universitair Docent
- Faculteit Wiskunde & Informatica, Data Mining
Persoon: UD : Universitair Docent
Projecten 2016 2018
- 1 Geëindigd
Interoperability of Heterogeneous IoT Platforms
Exarchakos, G., van Hout, J., Mocanu, D. C., van der Lee, T., van Mil, J., van Mil, J. & Exarchakos, G.
1/01/16 → 31/12/18
Project: Onderzoek direct
Onderzoeksoutput 2006 2020
Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling
Du, X., Pei, Y., Duivesteijn, W. & Pechenizkiy, M., 2020, (Geaccepteerd/In druk) In : Data Mining and Knowledge Discovery.Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review
Fairness in network representation by latent structural heterogeneity in observational data
Du, X., Pei, Y., Duivesteijn, W. & Pechenizkiy, M., 2020, (Geaccepteerd/In druk) Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review
On local and global graph structure mining
Pei, Y., 5 feb 2020, (Geaccepteerd/In druk) Eindhoven: Technische Universiteit Eindhoven. 198 blz.Onderzoeksoutput: Scriptie › Dissertatie 1 (Onderzoek TU/e / Promotie TU/e)
Prijzen
Third Prize - National Olympiad in Informatics
Decebal C. Mocanu (Ontvanger), 1997
Prijs: Anders › Discipline gerelateerd › Wetenschappelijk
Activiteiten 2016 2018
Machine Learning, better, together.
Joaquin Vanschoren (Spreker)8 dec 2018
Activiteit: Types gesprekken of presentaties › Genodigd spreker › Wetenschappelijk
Tutorial on Automatic Machine Learning
Frank Hutter (Spreker), Joaquin Vanschoren (Spreker)3 dec 2018
Activiteit: Types gesprekken of presentaties › Keynote spreker › Wetenschappelijk
Democratizing and Automating Machine Learning
Joaquin Vanschoren (Spreker)28 aug 2018
Activiteit: Types gesprekken of presentaties › Genodigd spreker › Wetenschappelijk
Pers/media
4. The internet will continue to make life better
28/10/19
1 item van Media-aandacht
Pers / media: Vakinhoudelijk commentaar
Data correlation helps recognize pickpockets
12/07/18
2 items van Media-aandacht
Pers / media: Vakinhoudelijk commentaar
Conference Interpretation Today
19/04/18
1 item van Media-aandacht
Pers / media: Vakinhoudelijk commentaar
Scripties/masterproeven
A framework for understanding business process remaining time predictions
Auteur: Verhoef, C., 28 okt 2019Begeleider: Pechenizkiy, M. (Afstudeerdocent 1) & Scheepens, R. J. (Afstudeerdocent 2)
Scriptie/masterproef: Master
Algorithms for center-based trajectory clustering
Auteur: van de L'Isle, N., 28 jan 2019Begeleider: Buchin, K. (Afstudeerdocent 1) & Driemel, A. (Afstudeerdocent 2)
Scriptie/masterproef: Master
Bestand
An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks
Auteur: Remmits, Y., 30 sep 2019Begeleider: Menkovski, V. (Afstudeerdocent 1) & Stolikj, M. (Externe coach)
Scriptie/masterproef: Master
Bestand