Abstract
The importance of general metric spaces in modeling of complex objects is increasing. A key aspect in testing of algorithms on general metric spaces is the generation of appropriate sample set of objects. The chapter demonstrates that the usual way, i.e. the mapping of elements of some vector space into general metric space is not an optimal solution. The presented approach maps the object set into the space of distance-matrixes and proposes a random walk sample generation method to provide a better uniform distribution of test elements.
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Acknowledgments
This research was carried out as part of the TAMOP-4.2.1.B-10/2/KONV-2010-0001 project with support by the European Union, co-financed by the European Social Fund and the technical background was supported by the Hungarian National Scientific Research Fund Grant OTKA K77809.
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Kovács, L. (2014). Generating Sample Points in General Metric Space. In: Iantovics, B., Kountchev, R. (eds) Advanced Intelligent Computational Technologies and Decision Support Systems. Studies in Computational Intelligence, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-319-00467-9_14
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DOI: https://doi.org/10.1007/978-3-319-00467-9_14
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