Description Usage Arguments Details Value Examples
Data sharpening based on K-nearest neighbors.
1 | shrinking(y, K, disMethod = "Euclidean", eps = 1e-04, itmax = 20)
|
y |
data matrix with rows being the observations and columns being variables. |
K |
number of nearest neighbors. |
disMethod |
specification of the dissimilarity measure. The available measures are “Euclidean” and “1-corr”. |
eps |
a small positive number. A value is regarded as zero if it is
less than |
itmax |
maximum number of iterations allowed. |
Within each iteration, each data point is replaced by the vector of the coordinate-wise medians of its K
nearest neighbors. Data points will
move toward the locally most dense data point by this shrinking process.
Sharpened data set.
1 2 3 4 5 6 7 8 9 10 | # Maronna data set
data(Maronna)
# data matrix
maronna <- Maronna$maronna
# cluster membership
maronna.mem <- Maronna$maronna.mem
tt <- shrinking(maronna, K = 5, itmax=1)
plotClusters(tt, maronna.mem)
|
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