18 results with keyword: 'how fast is the k means method'
We present polynomial upper and lower bounds on the number of iterations per- formed by the k -means method (a.k.a. Lloyd’s method) for k -means clustering.. Our upper bounds
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Suppose there are m factories called origins or sources produce aI (i=1,2……,m) units of products which are to be transported to n destinations with bJ (J=1,2,……,n) unity of
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A modified version of the fast global k -means (fast GKM) clustering method for clustering the gene expression datasets is proposed.. The fast GKM algorithm is
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Very little expander flange movement was observed while attaching the new piping. Since this was the desired effect, the monitoring showed a successful outcome. Offset value
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In search of our continued interest for the development of eco-friendly synthetic protocols in electrophilic substitution reactions, we have developed present methodology
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De Downlight is uit plastiek gemaakt en vermijdt dus elektrische schoks en glasbreuken Geen onderhoudskosten tijdens de hele
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Keywords- Wireless Sensor Network, K-means clustering method, Dynamic Network, distance functions, clustering, Euclidean
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A theory-based framework for strategic global human resource staffing policies and practices International. Journal of Human Resource Management ,
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The experimental results presented show that the ns- bounding scheme makes exact k-means algorithms faster, and that our Exponion algorithm is significantly faster than
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Fuzzy c means is compared with k means for image segmentation, in which Fuzzy c means gives higher accuracy than k means, Gabor Texture Extraction method is used to
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Lemma 2.1 yields an upper bound on the number of iterations that k-means needs: Since there are only few points close to hyperplanes, eventually a point switches from one cluster
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As the results showed average adjusted of posttest of components of time horizon after excluding the effect of pre-test time horizon in divorced women of the experimental group
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To analyze the accuracy of X-Means algorithm in comparison with other clustering algorithms like K-means fast and K-Medoids it is necessary to identify the number of
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Once the employer mandate provision becomes effective, a large employer will be subject to a penalty if any of its full-time employees receives a premium tax credit or
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For different network sizes, the k-means++ method is generating the initial centroids, then the HACO using k-means is implemented at different centroids to choose the best
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The proposed method is a hybrid technique based on parallel K-Means and parallel DBSCAN that combines the benefits of both parallel K-Means and parallel
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