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Existing hierarchical clustering algorithms involve a flat clustering component and an additional agglomerative or divisive procedure.
In contrast to flat clustering methods, which output a partitioning of the dataset, SAHN methods produce a similarity hierarchy, i.e., they present various flat clusterings with different granularities at once.
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These protocols comprise respectively the different approaches available for proactive protocols, employing hierarchical clustered routing, flat un-clustered routing and flat clustered routing, taking also into account the different approaches taken by the reactive AODV protocol.
The flat clusters of bright orange flowers crown stems that are between one and two feet tall.
Parsnips, covered with flat clusters of small flowers, loom 5 feet high and 3 feet across.
This spring, I planted a yarrow with flat clusters of brick-red flowers in the walled gravel garden at my New Jersey home.
Globe candytuft (Iberis umbellata) is a common garden annual that bears flat clusters of pink, violet, white, purple, or red flowers in late summer.
This scheme results in a flat clustered routing approach even though it bears resemblance to hierarchical routing because of the existing MPR clusters.
This library provides Python functions for agglomerative clustering, including the generation of hierarchical clusters from distance matrices, computation of distance matrices from observation vectors, calculation of cluster statistics, linkage cutting to generate flat clusters, and cluster visualization with dendrograms.
It stands to reason that, if hierarchical models describe the data better than flat cluster models, then such models will have higher discriminatory power.
A number of methods emerged that attempted to calibrate p-values based on global or local measures of average similarity [6] [9] or flat clusters based on early bifurcations of the phylogenetic tree [11].
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