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Specifically, instead of producing a single clustering they produce a hierarchy of clusterings.
This kind of algorithm determines a whole hierarchy of clustering, rather than a single clustering.
Most of the existing clustering methods have focused on optimizing a single clustering objective.
The experimental results demonstrate the ability of the method to identify possible segmentations in a dataset, compared to algorithms that only yield a single clustering solution.
Similar to a recent study evaluating clustering methods on several biological datasets26, we found that no single clustering is the best performer on all datasets, although certain methods are certainly better than others at retrieving the known modular structure within the data.
Cluster ensemble approaches make use of a set of clustering solutions which are derived from different data sources to gain a more comprehensive and significant clustering result over conventional single clustering approaches.
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These nodes were merged into single clusters.
First, the deposition of single cluster was simulated.
Bonnie and Brian's paper on single cluster junctions just published in Chemical Science.
Deconvolution further improved the resolution, resulting in two clearly separated clusters in a double clustered spine, but did not induce cluster artifacts (single cluster spine is still single clustered after deconvolution).
Bonnie and Giacomo's paper on current blockade in single cluster junctions was published in Nature Nanotechnology.
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