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This quick-start guide demonstrates how to generate multi-cluster high-dimensional data. We simulate three distinct \(4\text{-}D\) clusters with different shapes, scales, and rotations.
Each cluster can be rotated in a different way across specified \(2\text{-}D\) planes.
rotations_4d <- list(
cluster1 = list(
list(plane = c(1, 2), angle = 60), # Rotation in the (1, 2) plane
list(plane = c(3, 4), angle = 90) # Rotation in the (3, 4) plane
),
cluster2 = list(
list(plane = c(1, 3), angle = 30) # Rotation in the (1, 3) plane
),
cluster3 = list(
list(plane = c(2, 4), angle = 45) # Rotation in the (2, 4) plane
)
)
We use gen_multicluster()
to generate 3 clusters with
varying shapes and positions in \(4\text{-}D\) space.
clust_data <- gen_multicluster(n = c(200, 300, 500), p = 4, k = 3,
loc = matrix(c(
0, 0, 0, 0,
5, 9, 0, 0,
3, 4, 10, 7
), nrow = 3, byrow = TRUE),
scale = c(2, 5, 1),
shape = c("gaussian", "cone", "unifcube"),
rotation = rotations_4d,
is_bkg = FALSE
)
langevitour(clust_data |> dplyr::select(-cluster),
pointSize = 2, group = clust_data$cluster)
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They may not be fully stable and should be used with caution. We make no claims about them.
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