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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:
    Optimization of traveling salesman problem using affinity propagation clustering and genetic algorithm
    Autorzy:
    El-Samak, A. F.
    Ashour, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/91810.pdf
    Data publikacji:
    2015
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    combinatorial optimization problem
    travel salesman problem
    genetic algorithm
    evolutionary computation algorithm
    affinity propagation clustering technique
    AP
    problem optymalizacji kombinatorycznej
    algorytm genetyczny
    obliczenia ewolucyjne
    Opis:
    Combinatorial optimization problems, such as travel salesman problem, are usually NPhard and the solution space of this problem is very large. Therefore the set of feasible solutions cannot be evaluated one by one. The simple genetic algorithm is one of the most used evolutionary computation algorithms, that give a good solution for TSP, however, it takes much computational time. In this paper, Affinity Propagation Clustering Technique (AP) is used to optimize the performance of the Genetic Algorithm (GA) for solving TSP. The core idea, which is clustering cities into smaller clusters and solving each cluster using GA separately, thus the access to the optimal solution will be in less computational time. Numerical experiments show that the proposed algorithm can give a good results for TSP problem more than the simple GA.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 239-245
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Clustering large-scale data based on modified affinity propagation algorithm
    Autorzy:
    Serdah, A. M.
    Ashour, W. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/91694.pdf
    Data publikacji:
    2016
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    clustering
    clustering algorithm
    data clustering algorithm
    propagation algorithm
    Affinity Propagation
    AP
    klasteryzacja
    algorytm klastrowania
    algorytm propagacji
    Opis:
    Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering algorithm is performed on all local cluster exemplars to select well-suited global exemplars of the whole data set. Finally, all the data points are clustered by the similarity between all global exemplars and each data point. Results show that the proposed clustering method can significantly reduce the clustering time and produce better clustering result in a way that is more effective and accurate than AP, KAP, and HAP algorithms.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 1; 23-33
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Reservoir computing and data visualisation
    Autorzy:
    Ashour, W.
    Wang, T. D.
    Fyfe, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/91852.pdf
    Data publikacji:
    2012
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    reservoir computing
    data visualisation
    time series
    Echo State Networks
    ESNs
    multidimensional scaling criterion
    fixed latent space
    Opis:
    We consider the problem of visualisation of high dimensional multivariate time series. A data analyst in creating a two dimensional projection of such a time series might hope to gain some intuition into the structure of the original high dimensional data set. We review a method for visualising time series data using an extension of Echo State Networks (ESNs).The method uses the multidimensional scaling criterion in order to create a visualisation of the time series after its representation in the reservoir of the ESN. We illustrate the method with two dimensional maps of a financial time series. The method is then compared with a mapping which uses a fixed latent space and a novel objective function.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 3; 215-222
    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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