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Principal components analysis coupled with a cluster classification method and spatial overlay techniques were used to identify two hierarchical levels of landscapes.
On the other hand, the system parallelization has been divided into two levels: first, the parallelism of the classification has been exploited taking advantage of the pixel-wise classification methodology supported by the SVM algorithm and, secondly, a double-buffer communication procedure has been implemented to parallelize the image transmission and the cluster classification stages.
Data mining problem has different branches, like outlier detection, association rule, cluster, classification, prediction.
Cluster classification using hierarchical cluster analysis (HCA) is done by employing Euclidean distance and the Ward's linkage method.
The spike sorting method was based on cluster classification in reduced space.
The cluster classification enabled comparison between strains and serves as a tool for vaccine development [ 9].
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Clustering analysis uses Bayesian modeling techniques to simultaneously produce clusters and assign individuals to these "unsupervised" cluster classifications.
We followed these same cluster classifications for the samples in this analysis.
To ensure consistency in the cluster classifications, a review of the cluster classifications was made, in some cases a revised classification was proposed to the country and the revised classification was reviewed by each country.
Methods include time-series analysis; clustering, classification algorithms, genetic and Markov Chain Monte Carlo methods.
clustering, classification and learning (naive Bayes, support vector machines, k-means).
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