يعرض 1 - 20 نتائج من 142 نتيجة بحث عن '(( elements method algorithm ) OR ((( data clustering algorithm ) OR ( data hiding algorithm ))))*', وقت الاستعلام: 0.12s تنقيح النتائج
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    Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm حسب Abu Zitar, Raed

    منشور في 2022
    "…Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. …"
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    An enhanced k-means clustering algorithm for pattern discovery in healthcare data حسب Haraty, Ramzi A.

    منشور في 2015
    "…This paper studies data mining applications in healthcare. Mainly, we study k-means clustering algorithms on large datasets and present an enhancement to k-means clustering, which requires k or a lesser number of passes to a dataset. …"
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    article
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    Correlation Clustering with Overlaps حسب Fakhereldine, Amin

    منشور في 2020
    "…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …"
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    masterThesis
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    A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM حسب Selim, S.Z.

    منشور في 2020
    "…In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. …"
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    article
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    NEW ALGORITHMS FOR SOLVING THE FUZZY CLUSTERING PROBLEM حسب Kamel, M.S.

    منشور في 2020
    "…Two new algorithms for fuzzy clustering are presented. Convergence of the proposed algorithms is proved. …"
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    article
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    Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm حسب Nasser, Youssef

    منشور في 2024
    "…Among these, K-means is widely used for efficiently solving clustering problems. This work aims to enhance the performance of the K-means algorithm by introducing a novel method for selecting the initial centroids, thereby minimizing randomness and reducing the number of iterations needed to reach optimal results. …"
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    masterThesis
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