Arno Knobbe
Arno Knobbe is a former member of the ADA group.

Arno Knobbe has been employed as a post-doctoral researcher by the ADA group since November 2004, just after receiving his Ph.D. (also at Utrecht University). The subject of his thesis, which can be found here, was Multi-Relational Data Mining. His current research includes the following subjects:

Multi-Relational Data Mining
MRDM is the art of mining useful knowledge from structured data stored in relational databases. Because structured data requires a database which contains multiple related tables, traditional single-table techniques cannot be used. The field of MRDM is concerned with generalising common Data Mining concepts to a multi-table setting. Over five years of research in this area has resulted in the Safarii MRDM system, which includes a whole range of Data Mining facilities organised around a relational database containing multiple tables.

Arno Knobbe is involved in the European Embryonal Tumor Pipeline, an EU-funded research project concerning the genetic causes of several cancers in small children. Along with the Jozef Stefan Institute and the German Cancer Research Fund, he is responsible for the data analysis part of the project, with a specific focus on the integration of knowledge from multiple sources.

Local Patterns and Pattern Teams
Most modern pattern discovery algorithms produce an abundance of results. In many cases, the end-user has limited time and is only interested in finding the few key patterns in the data. Pattern Teams, a framework developed within the ADA group is concerned with finding these few non-redundant patterns within large collections of local patterns. The different Pattern Team solutions have been implemented on top of Safarii.

Learning in Computer Vision
A recent system developed by Arno Knobbe is the Computer Vision system called Harmonii. The system uses stereo-images to build up a 3D representation of the filmed scene, and plan trajectories for mobile robots in an automotive setting. Machine Learning algorithms are used to classify different areas of the image, based on colour, texture, 3D orientation etc., in order to recognize features such as road-surface en traffic signs.

A full list of publications since 1995 can be found here.

Selected Refereed Publications

Duivesteijn, W., Feelders, A., Knobbe, A. Different Slopes for Different Folks - Mining for Exceptional Regression Models with Cook's Distance. In: Proceedings KDD 2012, 2012.
van Leeuwen, M. & Knobbe, A.J. Diverse Subgroup Set Discovery. In: Data Mining and Knowledge Discovery, special issue ECMLPKDD'12, pp 242-208, Springer, 2012.
van Leeuwen, M. & Knobbe, A.J. Non-Redundant Subgroup Discovery in Large and Complex Data. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data 2011 (ECML PKDD'11), 2011.
Duivesteijn, W., Knobbe, A.J., Feelders, A. & van Leeuwen, M. Subgroup Discovery meets Bayesian networks – an Exceptional Model Mining approach. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10), 2010.
Knobbe, A., Crémilleux, B., Fürnkranz, J. & Scholz, M. From Local Patterns to Global Models: the LeGo Approach to Data Mining. In: Fürnkranz, J. and Knobbe (eds.): From Local Patterns to Global Models: Proceedings of the ECML PKDD 2008 Workshop (LEGO'08), pp 1-16, 2008.
Leman, D., Feelders, A. & Knobbe, A. Exceptional Model Mining. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data 2008 Part II (ECML PKDD'08), pp 1-16, 2008.
van de Koppel, E., Slavkov, I., Astrahantseff, K., Schramm, A., Schulte, J., Vandesompele, J., de Jong, E., Dzeroski, S., Knobbe, A. Knowledge Discovery in Neuroblastoma-related Biological Data. In: Data Mining in Functional Genomics and Proteomics Workshop (DMFGP'07), pp 45-56, 2007.
Knobbe, A.J. & Ho, E.K.Y. Pattern Teams. In: Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases 2006 (PKDD'06), pp 577-584, 2006.
Knobbe, A.J. & Ho, E.K.Y. Maximally Informative k-Itemsets and their Efficient Discovery. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2006 (KDD'06), pp 244-253, 2006.
Knobbe, A.J. Numbers in Multi-Relational Data Mining. In: Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases 2005 (PKDD'05), pp 544-551, 2005.

Selected Unrefereed Publications

Knobbe, A., Feelders, A., Leman, D. Exceptional Model Mining, Data Mining: Foundations and Intelligent Paradigms 2, Holmes, D., Jain, L. (eds.), 2011.