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Wyszukujesz frazę "fuzzy clustering" wg kryterium: Temat

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    Wyświetlanie 1-8 z 8
    Tytuł:
    An ε-Insensitive Approach to Fuzzy Clustering
    Autorzy:
    Łęski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908067.pdf
    Data publikacji:
    2001
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    programowanie
    metoda grupowania
    fuzzy clustering
    fuzzy c-means
    robust methods
    varepsilon-insensitivity
    fuzzy c-medians
    Opis:
    Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new varepsilon-insensitive Fuzzy C-Means (varepsilonFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2001, 11, 4; 993-1007
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An automatic hybrid method for retinal blood vessel extraction
    Autorzy:
    Yang, Y.
    Huang, S.
    Rao, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/907899.pdf
    Data publikacji:
    2008
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    naczynie krwionośne
    ekstrakcja
    obraz
    siatkówka
    morfologia matematyczna
    grupowanie rozmyte
    blood vessel extraction
    retinal image
    mathematical morphology
    fuzzy clustering
    Opis:
    The extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retinal image is smoothed and strengthened so that the blood vessels are enhanced and the background information is suppressed. The fuzzy clustering algorithm is then employed to the previous enhanced image for segmentation. After the fuzzy segmentation, a purification procedure is used to reduce the weak edges and noise, and the final results of the blood vessels are consequently achieved. The performance of the proposed method is compared with some existing segmentation methods and hand-labeled segmentations. The approach has been tested on a series of retinal images, and experimental results show that our technique is promising and effective.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2008, 18, 3; 399-407
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An automatic segmentation method for scanned images of wheat root systems with dark discolourations
    Autorzy:
    Gocławski, J.
    Sekulska-Nalewajko, J.
    Gajewska, E.
    Wielanek, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/930018.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system korzeniowy
    segmentacja
    szkielet
    klasteryzacja
    root system image
    segmentation
    skeleton
    root discolourations
    fuzzy c-means clustering
    Opis:
    The analysis of plant root system images plays an important role in the diagnosis of plant health state, the detection of possible diseases and growth distortions. This paper describes an initial stage of automatic analysis-the segmentation method for scanned images of Ni-treated wheat roots from hydroponic culture. The main roots of a wheat fibrous system are placed separately in the scanner view area on a high chroma background (blue or red). The first stage of the method includes the transformation of a scanned RGB image into the HCI (Hue-Chroma-Intensity) colour space and then local thresholding of the chroma component to extract a binary root image. Possible chromatic discolourations, different from background colour, are added to the roots from blue or red chroma subcomponent images after thresholding. At the second stage, dark discolourations are extracted by local fuzzy c-means clustering of an HCI intensity image within the binary root mask. Fuzzy clustering is applied in local windows around the series of sample points on roots medial axes (skeleton). The performance of the proposed method is compared with hand-labelled segmentation for a series of several root systems.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2009, 19, 4; 679-689
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An outlier-robust neuro-fuzzy system for classification and regression
    Autorzy:
    Siminski, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/1838201.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    outliers
    neuro-fuzzy system
    clustering algorithm
    regression
    wyjątki
    system neurorozmyty
    algorytm grupowania
    Opis:
    Real life data often suffer from non-informative objects—outliers. These are objects that are not typical in a dataset and can significantly decline the efficacy of fuzzy models. In the paper we analyse neuro-fuzzy systems robust to outliers in classification and regression tasks. We use the fuzzy c-ordered means (FCOM) clustering algorithm for scatter domain partition to identify premises of fuzzy rules. The clustering algorithm elaborates typicality of each object. Data items with low typicalities are removed from further analysis. The paper is accompanied by experiments that show the efficacy of our modified neuro-fuzzy system to identify fuzzy models robust to high ratios of outliers.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 2; 303-319
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neuro-rough-fuzzy approach for regression modelling from missing data
    Autorzy:
    Simiński, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/331298.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system neuronowo-rozmyty
    ANNBFIS
    brakujące wartości
    zbiór przybliżony
    zbiór rozmyty
    neuro-fuzzy
    missing values
    marginalisation
    imputation
    rough fuzzy set
    clustering
    Opis:
    Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 2; 461-476
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Relations of granular worlds
    Autorzy:
    Pedrycz, W.
    Vukovich, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/908015.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    elektrotechnika
    granular computing
    clustering
    models of collaborative computing
    fuzzy models
    collaboration and competition
    FCM
    granular modeling
    Opis:
    In this study, we are concerned with a two-objective development of information granules completed on a basis of numeric data. The first goal of this design concerns revealing and representing a structure in a data set. As such it is very much oriented towards coping with the underlying relational aspects of the experimental data. The second goal deals with a formation of a mapping between information granules constructed in two spaces (thus it concentrates on the directional aspect of information granulation). The quality of the mapping is directly affected by the information granules over which it operates, so in essence we are interested in the granules that not only reflect the data but also contribute to the performance of such a mapping. The optimization of information granules is realized through a collaboration occurring at the level of the data and the mapping between the data sets. The operational facet of the problem is cast in the realm of fuzzy clustering. As the standard techniques of fuzzy clustering (including a well-known approach of FCM) are aimed exclusively at the first objective identified above, we augment them in order to accomplish sound mapping properties between the granules. This leads to a generalized version of the FCM (and any other clustering technique for this matter). We propose a generalized version of the objective function that includes an additional collaboration component to make the formed information granules in rapport with the mapping requirements (that comes with a directional component captured by the information granules). The additive form of the objective function with a modifiable component of collaborative activities makes it possible to express a suitable level of collaboration and to avoid a phenomenon of potential competition in the case of incompatible structures and the associated mapping. The logic-based type of the mapping (that invokes the use of fuzzy relational equations) comes ...
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2002, 12, 3; 347-357
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ant-based clustering for flow graph mining
    Autorzy:
    Lewicki, Arkadiusz
    Pancerz, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/330782.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    possibly certain sequences
    flow graph
    rough set
    fuzzy set
    ant based clustering
    graf przepływu danych
    zbiór przybliżony
    zbiór rozmyty
    Opis:
    The paper is devoted to the problem of mining graph data. The goal of this process is to discover possibly certain sequences appearing in data. Both rough set flow graphs and fuzzy flow graphs are used to represent sequences of items originally arranged in tables representing information systems. Information systems are considered in the Pawlak sense, as knowledge representation systems. In the paper, an approach involving ant based clustering is proposed. We show that ant based clustering can be used not only for building possible large groups of similar objects, but also to build larger structures (in our case, sequences) of objects to obtain or preserve the desired properties.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 3; 561-572
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
    Autorzy:
    Soltani, M.
    Chaari, A.
    Ben Hmida, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/330134.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    model rozmyty Takagi-Sugeno
    algorytm grupowania
    metoda najmniejszych kwadratów
    optymalizacja rojem cząstek
    Takagi-Sugeno fuzzy models
    noise clustering algorithm
    fuzzy c-regression model
    orthogonal least squares
    particle swarm optimization (PSO)
    Opis:
    This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 617-628
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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