Fractal Based Data Separation in Data Mining

0389175 - ÚI 2013 RIV HK eng C - Konferenční příspěvek (zahraniční konf.)
Jiřina, Marcel - Jiřina jr., M.
Fractal Based Data Separation in Data Mining.
Proceedings of the The Third International Conference on Digital Information Processing and Communications. Hong Kong: SDIWC, 2013, s. 287-295. ISBN 978-0-9853483-3-5.
[ICDIPC 2013. International Conference on Digital Information Processing and Communications /3./. Dubai (AE), 30.01.2013-01.02.2013]
Grant CEP: GA MŠMT(CZ) LG12020
Institucionální podpora: RVO:67985807
Klíčová slova: nearest neighbor * fractal set * multifractal * IINC method
Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
http://sdiwc.net/digital-library/fractal-based-data-separation-in-data-mining

The separation of the searched data from the rest is an important task in data mining. Three separation/classification methods are presented. Considering data as points in a metric space, the methods are based on transformed distances of neighbors of a given point in a multidimensional space via a function that uses an estimate of scaling exponent. We shortly describe them and show that transformation function has form of the distance to the scaling exponent power. We also show the efficiency of methods presented on artificial as well as on real-life tasks and compare them with other standard as well as advanced approaches.
Trvalý link: http://hdl.handle.net/11104/0218064