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Sentences for "centroids "
First, the scan statistic evaluates geographic relationships using centroids and a circular scan window.
This arises because the local Moran and boundary analysis techniques use ZIP code zone adjacency (sharing of a common border) rather than distance between centroids to evaluate geographic relationships, and because the scale of the study differs.
In contrast, the scan technique, employed by New York State, uses ZIP code centroids and circles drawn around those centroids to represent Long Island's geography.
Because of its long and forked appearance, the geography of Long Island is poorly represented by centroids and circles.
The spatial scan is based on a likelihood statistic, it uses centroids of areas to define spatial relationships, employs circular scan areas, and is univariate.
Clearly, "clusters" can be multivariate, and not just univariate, they can be other shapes than circular, there are techniques other than centroids for evaluating spatial relationships, and likelihood is just one of several statistical approaches for identifying departures from a background morbidity.
Second, because the random initialization commonly used in k-means clustering can have a profound impact on the results [ 15], we instead chose to initialize each clustering cycle by seeding prototype centroids with the eigen vectors identified by PCA of the respective dataset (see below).
The prototype centroids are refined in the subsequent steps: each gene is assigned a membership score to each of the prototype centroids, based on the Pearson correlation between the gene's expression pattern and the centroid in question.
The process of calculating gene-centroid memberships and updating the centroids is iterated until the centroid patterns become fixed (or until the termination criterion is met, as described in Materials and methods).
7 correlation to any of the identified centroids are removed from the dataset (see Materials and methods).
The fuzzy k-means clustering steps described above are repeated on this smaller dataset to identify patterns missed in the first clustering cycle, and the new centroids are added to the set identified in the first round.
The process of averaging replicated centroids and selecting a data subset is repeated, and a third cycle of clustering is performed on the subset of genes with a correlation of less than 0.
The newly identified centroids are combined with the previous sets, and replicate centroids are averaged.
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