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 

  • 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.

Projecten 2016 2018

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.

1/01/1631/12/18

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 tijdschriftTijdschriftartikelAcademicpeer 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/CongresprocedureConferentiebijdrageAcademicpeer review

On local and global graph structure mining

Pei, Y., 5 feb 2020, (Geaccepteerd/In druk) Eindhoven: Technische Universiteit Eindhoven. 198 blz.

Onderzoeksoutput: ScriptieDissertatie 1 (Onderzoek TU/e / Promotie TU/e)

Prijzen

Third Prize - National Olympiad in Informatics

Decebal C. Mocanu (Ontvanger), 1997

Prijs: AndersDiscipline gerelateerdWetenschappelijk

Activiteiten 2016 2018

  • 3 Genodigd spreker
  • 1 Keynote spreker
  • 1 Bezoek externe academische instelling

Machine Learning, better, together.

Joaquin Vanschoren (Spreker)
8 dec 2018

Activiteit: Types gesprekken of presentatiesGenodigd sprekerWetenschappelijk

Tutorial on Automatic Machine Learning

Frank Hutter (Spreker), Joaquin Vanschoren (Spreker)
3 dec 2018

Activiteit: Types gesprekken of presentatiesKeynote sprekerWetenschappelijk

Democratizing and Automating Machine Learning

Joaquin Vanschoren (Spreker)
28 aug 2018

Activiteit: Types gesprekken of presentatiesGenodigd sprekerWetenschappelijk

Pers/media

4. The internet will continue to make life better

Joaquin Vanschoren

28/10/19

1 item van Media-aandacht

Pers / media: Vakinhoudelijk commentaar

Data correlation helps recognize pickpockets

Mykola Pechenizkiy

12/07/18

2 items van Media-aandacht

Pers / media: Vakinhoudelijk commentaar

Conference Interpretation Today

Mykola Pechenizkiy

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 2019

Begeleider: 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 2019

Begeleider: 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 2019

Begeleider: Menkovski, V. (Afstudeerdocent 1) & Stolikj, M. (Externe coach)

Scriptie/masterproef: Master

Bestand