Seminar by Dr. Arthur Zimek (Ludwig-Maximilians-University Munich, Germany)

Speaker: Dr. Arthur Zimek
                Ludwig-Maximilians-University Munich, Germany

Title: Ensembles for Unsupervised Outlier Detection: Challenges and Solutions

Date: Wednesday, March 9th, 2016

Time: 10:30-12pm

Place: EV3.309

ABSTRACT

We discuss the use of ensemble techniques for unsupervised outlier detection. To introduce the field, we will briefly sketch the data mining task of unsupervised outlier detection and discuss some basic considerations about ensemble techniques. We give an overview on existing approaches to using ensemble techniques for outlier detection as well as on the challenges in doing so. We focus then on three recent ensemble methods for outlier detection, featuring different meta-approaches to get diverse ensemble members.

BIO

Dr. Arthur Zimek is a Privatdozent in the database systems and data mining group at the Ludwig-Maximilians-Universität München (LMU), Germany. 2012-2013 he was a postdoctoral fellow in the department for Computing Science at the University of Alberta, Edmonton, Canada. In 2014 he was a visiting professor at Technical University Vienna, Austria. He holds degrees in bioinformatics, philosophy, and theology, involving studies at universities in Munich, Mainz (Germany), and Innsbruck (Austria) and finished his Ph.D. thesis in informatics on ''Correlation Clustering'' at LMU in summer 2008. For this work, Zimek received the ''SIGKDD Doctoral Dissertation Award (runner-up)'' in 2009. His research interests include ensemble techniques, clustering, and outlier detection, methods as well as evaluation, and high dimensional data. Zimek published more than 60 papers at peer reviewed conferences and in international journals. Together with his co-authors, he received the ''Best Paper Honorable Mention Award'' at SDM 2008 and the ''Best Demonstration Paper Award'' at SSTD 2011. Zimek has been a member or senior member of program committees of the leading data mining conferences (e.g. SIGKDD, ECMLPKDD, CIKM, SDM) and serves as reviewer for journals like ACM TKDD, IEEE TKDE, Data Mining and Knowledge Discovery (Springer), Machine Learning (Springer).