Algorithme de k-means
Accélération de k-means par pré-calcul dynamique d'agrégats
... de k-means en introduisant une approche d’optimisation basée sur le pré-calcul dynamique d’agrégats pouvant ensuite être réutilisés afin d’éviter des calculs ...
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La recherche et l’indexation des images par une hybridation d’une loi puissance et la méthode K-means
... Les K-Means Le codage des 9 classes ne permet pas toujours de bénéficier des 9 niveaux considérés, le présent codage, faisant appel à l’algorithme des k-means repose sur la même idée de ...
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Identification non-supervisée de pseudo-phones à l'aide de k-means et de réseaux convolutifs
... Actuellement, notre modèle a besoin de connaître les fron- tières des phonèmes ou phones afin d’uniformiser sur les seg- ments les information échangées entre l’algorithme des k-means et le CNN. Nous ...
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Identification non-supervisée de pseudo-phones à l'aide de k-means et de réseaux convolutifs
... Actuellement, notre modèle a besoin de connaître les fron- tières des phonèmes ou phones afin d’uniformiser sur les seg- ments les information échangées entre l’algorithme des k-means et le CNN. Nous ...
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Automatic detection of boundary layer height from Doppler lidar using K-means algorithm.
... l’hypothèse que les valeurs de turbulence et de concentration en aérosol sont significativement plus élevées dans la couche limite que dans l’atmosphère libre. En considérant un profil vertical où chaque point de mesure ...
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Sérialisation du k-means pour la segmentation des images en couleur
... l’algorithme k- means peuvent facilement laisser les centres des nu´ees se ...notre algorithme contre ce ph´enom`ene apr`es la stabilisation des centres dans chaque fenˆetre pour garantir la ...
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Optimal Variable Weighting for Ultrametric and Additive Trees and K-means Partitioning: Methods and Software
... des K centroïdes), les résultats des simulations indiquent qu’il est bon d’utiliser la méthode de pondération optimale des variables lors de l’analyse de tableaux de données susceptibles de contenir des ...
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Algorithme top-k pour la recherche d'information dans les réseaux sociaux
... by means of adjacency lists : for each vertex, we have a list of its neighbours and their associated weights (we can safely assume the list comes pre- sorted descending by ...
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Unsupervised Speech Unit Discovery Using K-means and Neural Networks
... Abstract. Unsupervised discovery of sub-lexical units in speech is a problem that currently interests speech researchers. In this paper, we report experiments in which we use phone segmentation followed by clus- tering ...
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Graph based k-means clustering
... a k dimensional binary search tree, referred to as the k − d tree ...optimized k − d tree method chooses the hyperplane passing through the median point perpendicular to the coordinate axis where the ...
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Generalized k-means based clustering for temporal data under time warp
... [KLT03]. k-means-based clustering, viz. standard k-means, k-means++, and all its variations, is among the most popular clustering algorithms, because it provides a good trade-off ...
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Asymptotic properties of K-means clustering algorithm as a density estimation procedure
... approach ") of the locally optimal sample k-means clusters... ASi'MPTOTIC PROPERTIES OF LOCALLY[r] ...
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NMR metabolic analysis of samples using fuzzy K-means clustering
... S101 NMR metabolic analysis of samples using fuzzy K-means clustering Figure 3. A. PCA of 1 H NMR spectral profiles of metabolites for the urine samples for rat, mouse and human healthy and diabetic ...
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Constrained Distance Based K-Means Clustering for Satellite Image Time-Series
... available k-Means constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is thought to be more appropriate for time-series ...
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Sparse k-means for mixed data via group-sparse clustering
... The hyper parameter λ may be tuned using various criteria for assessing the quality of the model, such as the gap statistic as described in [3], the ratio of explained variance, etc. Usually, one considers a fine grained ...
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Applying subclustering and Lp distance in Weighted K-Means with distributed centroids
... obtain the expected number of clusters. Note that all the four algorithms we consider here are non-deterministic. This means that we not only have to run them many times, but also that we have to find what the ...
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Comparison of K-means and GMM methods for contextual clustering in HSM
... the most suitable for this case, the feed rate Vf can be classified into 3 clusters by using these 3 thresholds. The same period 30s is always taken to be analyzed. Firstly, the classification by threshold T ∆N =1σ= ...
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Detection of influential observations on the error rate based on the generalized k-means clustering procedure
... Croux C., Filzmoser P., and Joossens K. (2008), Classification efficiencies for robust linear discriminant analysis, Statistica Sinica 18, pp. 581-599 Croux C., Haesbroeck G., and Joossens K. (2008), ...
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Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs
... Rather noteworthy is the fact that the hyperbolic k-means algorithm proposed in this work has generality. In need not be restricted to problems of RU-BBU association, but could be applied to any clustering ...
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Comparison of K-means and GMM methods for contextual clustering in HSM
... ong with the Δ e result is in F at the sum of posed to follo rying speed, th � seconds to a ussians). ors GMMs cluste mponent post e fitted model d for example be used to cla d. The [r] ...
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