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    Wyświetlanie 1-4 z 4
    Tytuł:
    Decision-making enhancement in a big data environment : application of the K-means algorithm to mixed data
    Autorzy:
    Koren, Oded
    Hallin, Carina Antonia
    Perel, Nir
    Bendet, Dror
    Powiązania:
    https://bibliotekanauki.pl/articles/91712.pdf
    Data publikacji:
    2019
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    big data
    mixed data
    hadoop
    K-means
    decision making
    Opis:
    Big data research has become an important discipline in information systems research. However, the flood of data being generated on the Internet is increasingly unstructured and non-numeric in the form of images and texts. Thus, research indicates that there is an increasing need to develop more efficient algorithms for treating mixed data in big data for effective decision making. In this paper, we apply the classical K-means algorithm to both numeric and categorical attributes in big data platforms. We first present an algorithm that handles the problem of mixed data. We then use big data platforms to implement the algorithm, demonstrating its functionalities by applying the algorithm in a detailed case study. This provides us with a solid basis for performing more targeted profiling for decision making and research using big data. Consequently, the decision makers will be able to treat mixed data, numerical and categorical data, to explain and predict phenomena in the big data ecosystem. Our research includes a detailed end-to-end case study that presents an implementation of the suggested procedure. This demonstrates its capabilities and the advantages that allow it to improve the decision-making process by targeting organizations’ business requirements to a specific cluster[s]/profiles[s] based on the enhancement outcomes.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 4; 293-302
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Small Area Estimation Under a Mixture Model
    Autorzy:
    Chandra, Hukum
    Bathla, HVL
    Sud, U.C.
    Powiązania:
    https://bibliotekanauki.pl/articles/465788.pdf
    Data publikacji:
    2010
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Linear mixed model
    Small area estimation
    EBLUP
    Zero-inflated data
    mixture model
    Opis:
    Small area estimation (SAE) under a linear mixed model may not be efficient if data contain substantial proportion of zeros than would be expected under standard model assumptions (hereafter zero-inflated data). We discuss the SAE for zero-inflated data under a mixture model (Fletcher et al., 2005 and Karlberg, 2000) that account for excess zeros in the data. Our results from simulation studies show that mixture model based approach for SAE works well and produces an efficient set of small area estimates. An application to real survey data from the National Sample Survey Organisation of India demonstrates the satisfactory performance of the approach.
    Źródło:
    Statistics in Transition new series; 2010, 11, 3; 76-89
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessment of mixed network processes with shared inputs and undesirable factors
    Autorzy:
    Nematizadeh, Maryam
    Amirteimoori, Alireza
    Kordrostami, Sohrab
    Vaez-Ghasemi, Mohsen
    Powiązania:
    https://bibliotekanauki.pl/articles/406305.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    network data envelopment analysis
    parallel-series mixed networks
    weak disposability
    undesirable factors
    ranking
    Opis:
    In the real world, there are processes whose structures are like a parallel-series mixed network. Network data envelopment analysis (NDEA) is one of the appropriate methods for assessing the performance of processes with these structures. In the paper, mixed processes with two parallel and series components are considered, in which the first component or parallel section consists of the shared inputs, and the second component or series section consists of undesirable factors. By considering the weak disposability assumption for undesirable factors, a DEA approach as based on network slackbased measure (NSBM) is introduced to evaluate the performance of processes with mixed structures. The proposed model is illustrated with a real case study. Then, the model is developed to discriminate efficient units.
    Źródło:
    Operations Research and Decisions; 2020, 30, 1; 97-118
    2081-8858
    2391-6060
    Pojawia się w:
    Operations Research and Decisions
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Percentile-Adjusted Estimation of Poverty Indicators for Domains Under Outlier Contamination
    Autorzy:
    Veijanen, Ari
    Lehtonen, Risto
    Powiązania:
    https://bibliotekanauki.pl/articles/466042.pdf
    Data publikacji:
    2011
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    small area estimation
    poverty indicator
    income data
    bias correction
    auxiliary information
    mixed model
    prediction
    Opis:
    Traditional estimation of poverty and inequality indicators, such as the Gini coefficient, for regions does not currently use auxiliary information or models fitted to income survey data. A predictor-type estimator constructed from ordinary mixed model predictions is not necessarily useful, as the predictions have too small spread for estimation of income statistics. Ordinary bias corrections are aimed at correcting the expectation of predictions, but poverty indicators would not be affected at all by a correction involving multiplication of predictions. We need a method improving the shape of the distribution of predictions, as poverty indicators describe differences of income between people. We therefore introduce a transformation bringing the percentiles of transformed predictions closer to the percentiles of sample values. The experiments show that the transformation results in smaller MSE of a predictor. If unit-level data from population are not available, the marginal domain frequencies of qualitative auxiliary variables can be successfully incorporated into a new calibration-based predictor-type estimator. The results are based on design-based simulation experiments where we use a population generated from an EU-wide income survey. The study is a part of the AMELI project funded by the European Union under the Seventh Framework Programme for research and technological development (FP7).
    Źródło:
    Statistics in Transition new series; 2011, 12, 2; 345-356
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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