- author = "Vladan Babovic and Maarten Keijzer",
- title = "Computer supported knowledge discovery - A case study in flow resistance induced by vegetation",
- booktitle = "Proceedings of the XXVIII Congress of International Association for Hydraulic Research",
- year = "1999",
- address = "Graz, Austria",
- month = "22-27 " # aug,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.iahr.org/membersonly/grazproceedings99/pdf/C021.pdf",
- size = "7 pages",
-
abstract = "Data Mining and Knowledge Discovery aims at providing
tools to facilitate the conversion of data to a better
understanding of processes that generated or produced
those data. We call this the mining of data for
knowledge. Data mining extracts patterns from data. It
creates models from data, by using for example, genetic
programming, polynomial or artificial neural networks,
or even support vector machines. These new models,
combined with the understanding of the physical
processes - the theory - can result in an improved
understanding and novel formulations of physical laws
and an improved predictive capability.
The present paper describes some of the very first efforts under the D2K (Data to Knowledge) Research Project currently conducted at Danish Hydraulic Institute with a support from the Danish Technical Research Council (STVF). The paper firstly outlines elementary data mining principles, particularly when applied to analysis of scientific data. In the second half of the contribution, results obtained through analysis of the data related to the additional resistance to the flow induced by flexible vegetation are presented. The data are analysed by the means of genetic programming (GP). Induced formulations and discussed in terms of accuracy and physical interpretability.",
