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Then, the disconnectivity of each line cluster is computed.
Those points belong either to line cluster 1 or to line cluster 2 and thus C N ( y ) = 2.
In that case, those points belong either to line cluster with index 1 or to line cluster with index 2. Thus, C N ( y ) = 2.
Let C ( x ) be the index of the line cluster point where x belongs to.
The orange point corresponds to the nearest neighbor of y among the points of neighbor line cluster (black points).
d is the minimum distance between the aforementioned nearest neighbor and the extreme points of its line cluster.
Similar(45)
In Figure 11, two line clusters are shown ( C ( x ) = 1 and C ( x ) = 2 ).
If Eq. (3) is valid, then all points should line clustered along the ((1-S/S_0)=C) straight line.
In this paper we define a model, based on log line clustering and Markov chain simulation to create this synthetic log data.
In order to characterize point y, we define C N ( y ) as the number of distinct line clusters that points x ∈ N ( y ) belong to.
In addition to using the K-means algorithm to form appropriate point-clusters, the Rank Order Clustering (ROC) technique is used for the first time in mapping, where no preset number of clusters is required for recognizing line clusters.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com