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Clustering is the process of partitioning a given set into c distinct disjoint subsets called clusters such that elements (e.g. reads) on the same cluster have minimum distance between them and maximum distance with elements of different clusters.
Given a parameter k, this unsupervised learning method aims at partitioning the data points into k different families (clusters) such that elements in the same cluster are as similar to each other as possible.
Phase 1: Partition the network area into equal clusters such that each cluster contains M nodes.
We also assume that events randomly occur in faraway clusters such that.
The system divides the network into clusters, such that nodes are assigned into different groups with unique identifiers.
It begins with initializing centroids randomly and then allocates data points to clusters such that the square-error is minimized.
The inverse problem is how to split a given dataset into two clusters such that the margin between the two clusters attains the maximum.
Clustering is the task of grouping objects into clusters such that objects in the same cluster are more related or similar.
The step of mode seeking is therefore succeeded by the merging step that concatenates similar clusters such that a merged output is obtained.
It partitions m objects into r clusters such that each object belongs to the closest cluster in terms of mean distance.
The delay taps are grouped into clusters such that scattered paths within the same cluster possess the same random characteristics of the AOA to the receiver.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com