Dr Tijl De Bie
Dr Tijl De Bie, Bristol University, UK.
Bio:Tijl De Bie was appointed Lecturer in Artificial Intelligence at the University of Bristol in January 2007. Before that, he was a research assistant at the K.U.Leuven (Belgium) and the University of Southampton. He completed his PhD on machine learning and advanced optimization techniques in 2005 at the University of Leuven. During his PhD he spent research visits in Imperial College London, U.C. Berkeley, and U.C. Davis.His main research interests include statistical pattern analysis algorithms, the use of optimization theory to design such algorithms, and their practical application to bioinformatics, music information retrieval, and web and text mining. He has widely published in these areas, and has developed software tools that are currently used in genomic laboratories. He has been invited to lecture about pattern analysis in international conferences, and was a co-organizer of various workshops and summer schools on machine learning and pattern analysis.
Title: Subjective interestingness in exploratory data mining
Abstract: Exploratory data mining has as its aim to assist a user in improving his or her understanding about the data. Considering this aim, it seems self-evident that in this process the data as well as the user need to
be considered. Yet, the vast majority of exploratory data mining methods (including most methods for clustering, itemset and association rule mining, subgroup discovery, dimensionality reduction, etc) formalize
interestingness of patterns in an objective manner, disregarding the user altogether. More often than not this leads to subjectively uninteresting patterns being reported.In this talk I will discuss a general mathematical framework for formalizing interestingness in a subjective manner. I will further demonstrate how it can be successfully instantiated for a variety of exploratory data mining problems. Finally, I will outline some of the
challenges and research opportunities ahead.