K3: Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining

Heikki Mannila, Nokia Research Center and Helsinki University of Technology

Data mining research has approached the problems of analyzing large data sets in two ways. Simplifying a lot, the approaches can be characterized as follows. The database approach has concentrated on figuring out what types of summaries can be computed fast, and then finding ways of using those summaries. The model-based approach has focused on first finding useful model classes and then fast ways of fitting those models. In this talk I discuss some examples of both and describe some recent developments which try to combine the two approaches.

Presenter Bio

Heikki Mannila is research fellow at Nokia Research Center and a professor of computer science at the Helsinki University of Technology. He has previously been a professor at the University of Helsinki, researcher at Max Planck Institute of Computer Science and a senior researcher at Microsoft Research. He is editor-in-chief of Data Mining and Knowledge Discovery and an associate editor of ACM Transactions on Internet Technology. His research interests include data mining, databases, algorithms, bioinformatics and ubiquitous computing.