Универзитетска библиотека 'Никола Тесла', Ниш (УБНИ)
  • Two-Dimensional GMM-Based Clustering in the Presence of Quantization Noise
    Jovanović, Aleksandra Ž., 1971- = Јовановић, Александра Ж., 1971- ; Perić, Zoran, 1964- = Перић, Зоран, 1964-
    In this paper, unlike to the commonly considered clustering, wherein data attributes are accurately presented, it is researched how successful clustering can be performed when data attributes are ... represented with smaller accuracy, i.e. by using the small number of bits. In particular, the effect of data attributes quantization on the two- dimensional two-component Gaussian mixture model (GMM)-based clustering by using expectation–maximization (EM) algorithm is analyzed. An independent quantization of data attributes by using uniform quantizers, with the support limits adjusted to the minimal and maximal attribute values, is assumed. The analysis makes it possible to determine the number of bits for data presentation that provides the accurate clustering. These findings can be useful in clustering wherein before being grouped the data have to be represented with a finite small number of bits due to their transmission through the bandwidth-limited channel.
    Врста грађе - чланак, саставни део
    Година - 2021
    Језик - енглески
    COBISS.SR-ID - 57703945