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One Day Seminar: Data Mining in Practice18. February 2003
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Sponsored by:
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The use of very large databases has enhanced in the last years from supporting transactions to additionally reporting business trends. Moving further beyond on-line analytical processing, knowledge discovery (data mining) is capable of
Several data mining algorithms and toolboxes exist. However, before they can be applied, several steps are to be done: data cleaning, removing NULL values, feature generation and selection, choosing the appropriate data mining algorithm for the task and transforming the given data into its input format. Real-world applications of knowledge discovery show that up to 80% of the efforts are spent in pre-processing. The Mining Mart project has developed a methodology for easing the use of data mining by enhancing pre-processing. Join us for a one day seminar to learn about the practical use of data mining and the results of the Mining Mart project! Listen to the practical experience of internationally acknowledged researchers! Register via e-mail by simply stating you will attend the seminar and giving your full address. Registration is free of charge! |
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ProgramThe Mining Mart approach to data mining Case presentations
Integrating knowledge discovery into knowledge management SpeakersProf. Dr. Katharina Morik Since 1991 full professor. She has been a member of several program committees of international conferences on machine learning, inductive logic programming, and knowledge discovery. She organised the "First European Summer School on Machine Learning" together with Yves Kodratoff in 1988, the "European Conference on Machine Learning" as program chair in 1989, a workshop on "Machine Learning Applications" at the IFIP world congress in 1994, and an "Intensive Course on Data Mining and Knowledge Discovery" in 1999. Within the Network of Excellence for Knowledge Discovery she represents the training committee. She is coordinator of the IST-11993 Mining Mart project. Dr. Michael May Dr. Michael May is head of AiS Knowledge Discovery Team and leads the research efforts at the intersection of data mining and knowledge discovery, visual knowledge exploration, and databases. His current research interests are spatial data mining, the application of data mining and data warehousing to the analysis of biological data, and the formalization of causal reasoning processes. He is coordinator of the IST-10536-SPIN! project, for geographic information systems and spatial data mining. Dr. Jörg-Uwe Kietz Having received his Ph. D. on relational data analysis in 1996, he worked
as research scientist at Swiss Life, leading the internal data mining
project. In 2002 he moved to the kdlabs ag (Knowledge discovery and application). (Telecom Italia Lab, Italy) He joined the Artificial Intelligence Research Unit of Italian Telecom
Research in 1993 and worked on data mining techniques and multivariate
statistical analysis. His focus has been on data mining solutions to marketing
related problems, e.g., customer profiling and segmentation, marketbasket
analysis, or diffusion forecasting of innovation. He received his Ph. D. (1997) in Computer Science from the Technical
University of Warsaw. He holds a position as assistant professor at the
Warsaw University of Technology and is the leader of a research group
on applications of decision support systems at NIT. Since 1990 full professor. She has been a member of several program committees
of international conferences on machine learning and artificial intelligence.
She has been chairperson of the ?International Conference on Machine Learning?
(1996) and of the 4th ?International Workshop on Multistrategy Learning?
(1998). She represents the research committee inside the Network of Excellence
for Knowledge Discovery.
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