يعرض 1 - 20 نتائج من 118 نتيجة بحث عن '(((( food loading algorithm ) OR ( data clustering algorithm ))) OR ( element data algorithm ))', وقت الاستعلام: 0.14s تنقيح النتائج
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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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    Distributed dimension reduction algorithms for widely dispersed data حسب Abu-Khzam, F.N.

    منشور في 2002
    "…It is well known that information retrieval, clustering and visualization can often be improved by reducing the dimensionality of high dimensional data. …"
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    conferenceObject
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