Sentence examples for affinity cluster from inspiring English sources

Using what he calls an "affinity cluster" approach, which emphasized a network of pages based on states, constituencies and so on, the company programmed content across the network, tweaking the type of content based on feedback and engagement from fans.

In the experiments, we only focus on the situations that the damping factor is in the range of [0.5, 0.9] because only the damping factor falling into this range can guarantee that the affinity clustering results converge when the clustering process ended.

Furthermore, because the affinity clustering can effectively lead to a reduction of the computational cost for the RP searching which is involved in the probability distribution-aided positioning algorithm, the proposed algorithm can lower the difficulty and minimize the power consumption when estimating the user's position.

We used the R package DiffBind (http://www.bioconductor.org) to perform affinity clustering analysis of PeakRanger peak sets, in order to cluster the ChIP-seq samples based on tag density.

Formation of MCGs will be aided by the affinity clustering technique [ 54, 67], which can be employed in large samples and has the advantage of identifying an "exemplar" patient whose variables represent those of the MCG members, thus making it possible to characterize MCGs at the analysis stage.

For the highest-affinity clusters, ITC was used to analyze the complexation thermodynamics (Table  1).

Table 2 compares the clustering stage of our MAP clustering method to hierarchical clustering and affinity propagation clustering.

Recently one innovative clustering method called affinity propagation clustering (APC) [ 13] has been developed, which has shown promising results for clustering data points.

Table 2 Best performances reached by three clustering algorithms with the same dissimilarity matrix Videos MAP HAC AP 1 90.9 88.9 76.5 2 81.5 73.4 67.5 3 76.5 67.3 57.6 4 98.1 88.8 82.3 5 98.1 92.3 81.9 6 77.7 79.3 54.0 MAP, presented method; HAC, hierarchical clustering, and AP, affinity propagation clustering.

To identify functional modules, we tried three separate algorithms – hierarchical clustering [ 23], affinity propagation clustering [ 24] and ClusterOne [ 25].

In this paper, we aim at tackling the above-mentioned limitations by proposing a novel method that combines a clustering method with GibbsOS to discover the hidden regulation mechanism; the clustering method is called motif-guided affinity propagation clustering (mAPC) [ 2], a modified version of affinity propagation clustering (APC) [ 13].