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    Wyświetlanie 1-4 z 4
    Tytuł:
    DSMK-means “density-based split-and-Merge K-means clustering algorithm
    Autorzy:
    Aldahdooh, R. T.
    Ashour, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/91719.pdf
    Data publikacji:
    2013
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    clustering
    K-means
    Density-based Split
    Merge K-means clustering Algorithm
    DSMK-means
    clustering algorithm
    Opis:
    Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. This paper introduces Density-based Split- and -Merge K-means clustering Algorithm (DSMK-means), which is developed to address stability problems of standard K-means clustering algorithm, and to improve the performance of clustering when dealing with datasets that contain clusters with different complex shapes and noise or outliers. Based on a set of many experiments, this paper concluded that developed algorithms “DSMK-means” are more capable of finding high accuracy results compared with other algorithms especially as they can process datasets containing clusters with different shapes, densities, or those with outliers and noise.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 51-71
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Geodesic distances for clustering linked text data
    Autorzy:
    Tekir, S.
    Mansmann, F.
    Keimer, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/91737.pdf
    Data publikacji:
    2012
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    clustering
    geodesic distance
    text data
    k-means algorithm
    cosine distance
    k-harmonic means
    microprecision values
    Opis:
    The quality of a clustering not only depends on the chosen algorithm and its parameters, but also on the definition of the similarity of two respective objects in a dataset. Applications such as clustering of web documents is traditionally built either on textual similarity measures or on link information. Due to the incompatibility of these two information spaces, combining these two information sources in one distance measure is a challenging issue. In this paper, we thus propose a geodesic distance function that combines traditional similarity measures with link information. In particular, we test the effectiveness of geodesic distances as similarity measures under the space assumption of spherical geometry in a 0-sphere. Our proposed distance measure is thus a combination of the cosine distance of the term-document matrix and some curvature values in the geodesic distance formula. To estimate these curvature values, we calculate clustering coefficient values for every document from the link graph of the data set and increase their distinctiveness by means of a heuristic as these clustering coefficient values are rough estimates of the curvatures. To evaluate our work, we perform clustering tests with the k-means algorithm on a subset of the EnglishWikipedia hyperlinked data set with both traditional cosine distance and our proposed geodesic distance. Additionally, taking inspiration from the unified view of the performance functions of k-means and k-harmonic means, min and harmonic average of the cosine and geodesic distances are taken in order to construct alternate distance forms. The effectiveness of our approach is measured by computing microprecision values of the clusters based on the provided categorical information of each article.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 3; 247-258
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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:
    Performance comparison of hybrid electromagnetism-like mechanism algorithms with descent method
    Autorzy:
    Miyajima, H.
    Shigei, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/91563.pdf
    Data publikacji:
    2015
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    electromagnetism-like mechanism
    EM
    meta-heuristcs
    descent method
    BP
    k-means
    FIS
    hybrid EM method
    metaheurystyka
    metoda k-średnich
    metoda hybrydowa EM
    Opis:
    Electromagnetism-like Mechanism (EM) method is known as one of metaheuristics. The basic idea is one that a set of parameters is regarded as charged particles and the strength of particles is corresponding to the value of the objective function for the optimization problem. Starting from any set of initial assignment of parameters, the parameters converge to a value including the optimal or semi-optimal parameter based on EM method. One of its drawbacks is that it takes too much time to the convergence of the parameters like other meta-heuristics. In this paper, we introduce hybrid methods combining EM and the descent method such as BP, k-means and FIS and show the performance comparison among some hybrid methods. As a result, it is shown that the hybrid EM method is superior in learning speed and accuracy to the conventional methods.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 271-282
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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