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Our results raise important questions about interpreting regulatory module predictions obtained by finding clusters of conserved binding sites.
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Q-SiteFinder works by binding hydrophobic (CH3) probes to the protein, and then finding clusters of probes with the most favorable binding energy.
Therefore, SPN-based image clustering is a typical problem of finding clusters of different densities.
The subspace clustering is the process of finding clusters in the subspaces of the dataset.
Current efforts are focused on finding clusters of patients with similar characteristics and designing one implant for each cluster.
The described approach includes an exhaustive search procedure to find the group cluster parameters that resulted in an optimal clustering of the group data, by finding a clustering solution with a minimal normalized cut cost [20].
The clustering method first yields an estimate for the number of users, which is determined by finding the cluster centers through the subtractive clustering algorithm outlined in [19, 20].
We also calculate the average best-match of a clustering by finding the average of best-match values for all clusters in the reference clustering.
In our previous method [12], the major cluster from which the centroid to be used as model was determined by finding the cluster with the biggest number of samples in it.
You diagnose shingles by finding that characteristic cluster of lesions confined to a single dermatome — the medical term for that patch of skin that is neurologically supplied by a single nerve root.
Anchor reads are clustered by finding the set of maximal overlapping intervals, i.e. sets of reads for which all intervals are overlapping.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com