Sentence examples for fast cluster from inspiring English sources

To overcome these shortcomings, in this paper, we propose a fast cluster motif finding algorithm, named FCmotif, to solve the (l, d) motif identification problem in large scale ChIP data set.

In order to improve the efficiency, we propose a fast cluster motif finding algorithm, named as FCmotif, to identify the (l,   d) motifs in large scale ChIP-seq data set.

The three state variables in the model constitute a fast cluster with two apparent conserved moieties, which may be represented by the following relations (15) l = L 1 L 2 = 1 0 1 0 1 1 A B C = M f x f, where the lumped state variables L1 and L2 are introduced.

If the w state variables of a fast cluster are replaced by a single state variable x l L ′ = ∑ i = 1 w x l i, we obtain a reduced version of the original model ℳ r : ẋ r = A r x r + B r u y = C r x r + D u, where x r ∈ ℝ(n - w + 1), A r ∈ ℝ(n - w + 1) × (n - w + 1), B r ∈ ℝ(n - w + 1) × m, and C r ∈ ℝ l × (n - w + 1).

The speed is a scalar defining how fast clusters move.

During clustering, three texture features including energy, entropy, and contrast are selected for mean shift fast clustering.

In order to keep our approach suitable for large networks, the choice of a fast clustering algorithm was necessary.

We show a fast clustering and search operations for correct panel utilization based on size quantization as design variable and implemented via Voronoi segmentation.

However, in mission-critical applications, a fast clustering scheme, which can not only gather sensory data immediately after deployment but also reduce the energy consumption, is more desired.

BioDICE is a new plugin for the Taverna workbench, that can be adopted to perform fast clustering of multidimensional biological datasets and to generate their interactive visualization.

An automatic and fast clustering algorithm is employed to recognize the vortex-induced vibrations (VIVs) on the bridge deck in long-term monitoring datasets.