- Big Bang-Like Phenomenon in Multidimensional Data
Number of the records: 1  

Big Bang-Like Phenomenon in Multidimensional Data

  1. 1.
    SYSNO ASEP0427692
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleBig Bang-Like Phenomenon in Multidimensional Data
    Author(s) Jiřina, Marcel (UIVT-O) SAI, RID
    Source TitleProceedings of the International Conference on Computing Technology and Information Management ICCTIM 2014. - Wilmington : SDIWC, 2014 / Malakooti M.V. - ISBN 978-0-9891305-5-4
    Pagess. 262-269
    Number of pages8 s.
    Publication formMedium - C
    ActionICCTIM 2014. International Conference on Computing Technology and Information Management
    Event date09.04.2014-11.04.2014
    VEvent locationDubai
    CountryAE - United Arab Emirates
    Event typeWRD
    Languageeng - English
    CountryUS - United States
    KeywordsBig Bang ; scaling ; correlation dimension ; expansion of distances ; polynomial transformation
    Subject RIVBB - Applied Statistics, Operational Research
    R&D ProjectsLG12020 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
    Institutional supportUIVT-O - RVO:67985807
    AnnotationNotion of the Big Bang in Data was introduced, when it was observed that the quantity of data grows very fast and the speed of this growth rises with time. This is parallel to the Big Bang of the Universe which expands and the speed of the expansion is the larger the farther the object is, and the expansion is isotropic. We observed another expansion in data embedded in metric space. We found that when distances in data space are polynomially expanded with a proper exponent, the space around any data point displays similar growth that is the larger the larger is the distance. We describe this phenomenon here on the basis of decomposition of the correlation integral. We show that the linear rule holds for logarithm of distance from any data point to another and proportionality constant is the scaling exponent, especially the correlation dimension. After this transformation of distances the data space appears as locally uniform and isotropic.
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Year of Publishing2015
Number of the records: 1  

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