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In order to construct accuracy metamodel, the fuzzy based progressive metamodeling method was implemented to cluster samples generated by BBNS several patches in optimization domain.
DBSCAN was implemented to cluster potential binding sites locally within protein targets for the Pilot dataset.
"Proposed graph based clustering" discusses in detail the seven features used to detect botnets and the clustering methodology implemented to cluster these features.
This section discusses in detail the seven features used to detect botnets and the clustering methodology implemented to cluster these features.
A Bayesian Fourier Clustering method was implemented to cluster rhythmic genes according to their expression waveform.
Two programs, Single Linkage Cluster (SLC) and Markov Cluster Algorithm (MCL) were implemented to cluster genes into orthologous clusters.
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A Bernoulli model was implemented to identify clusters of positive farms, followed by a Normal model to detect clustering of farms with high prevalence.
Hierarchical clustering was implemented to group pathways into clusters based on the crosstalk matrix.
Self-organizing maps (SOM) and Principal Component Analysis (PCA) methods were implemented to generate visual clustering and to find out the correlations between experimental data.
Clustering, implemented to reduce data redundancy and improve sequence quality and length, grouped ESTs into contigs which were further organized into clusters (Table 1), providing a non-redundant catalog of represented genes.
The previously mentioned knowledge bank approaches are implemented to form clinically relevant clusters, whose size depends on the variables that are used.
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