Kernel mean shift clustering
Kernel mean shift clustering for 2- to 6-dimensional data.
kms(x, y, H, max.iter=400, tol.iter, tol.clust, min.clust.size, merge=TRUE,
keep.path=FALSE, verbose=FALSE)
## S3 method for class 'kms'
summary(object, ...)x |
matrix of data values |
y |
matrix of candidate data values for which the mean shift will
estimate their cluster labels. If missing, |
H |
bandwidth matrix/scalar bandwidth. If missing,
|
max.iter |
maximum number of iterations. Default is 400. |
tol.iter |
distance under which two successive iterations are
considered convergent. Default is 0.001*min marginal IQR of |
tol.clust |
distance under which two cluster modes are considered
to form one cluster. Default is 0.01*max marginal IQR of |
min.clust.size |
minimum cluster size (cardinality). Default is |
merge |
flag to merge clusters which are smaller than
|
keep.path |
flag to store the density gradient ascent paths. Default is FALSE. |
verbose |
flag to print out progress information. Default is FALSE. |
object |
object of class |
... |
other parameters |
Mean shift clustering belongs to the class of modal or density-based clustering methods. The mean shift recurrence of the candidate point x is x_j+1 = x_j + H D hat(f)(x_j)/hat(f)(x_j) where j>=0 and x_0 = x. The sequence x_0, x_1, ... follows the density gradient ascent paths to converge to a local mode of the density estimate hat(f). Hence x is iterated until it converges to its local mode, and this determines its cluster label.
The mean shift recurrence is terminated if successive iterations are
less than tol.iter or the maximum number of iterations
max.iter is reached. Final iterates which are less than
tol.clust distance apart are considered to form a single
cluster. If merge=TRUE then the clusters whose cardinality is less
than min.clust.size are iteratively merged with their nearest cluster.
If the bandwidth H is missing, then
the default bandwidth is the plug-in selector for the density gradient
Hpi(x,deriv.order=1). Any bandwidth that is suitable for the
density gradient is also suitable for the mean shift.
A kernel mean shift clusters set is an object of class kms
which is a list with fields:
x,y |
data points - same as input |
end.points |
matrix of final iterates starting from |
H |
bandwidth matrix |
label |
vector of cluster labels |
nclust |
number of clusters |
nclust.table |
frequency table of cluster labels |
mode |
matrix of cluster modes |
names |
variable names |
tol.iter,tol.clust,min.clust.size |
tuning parameter values - same as input |
path |
list of density gradient ascent paths where |
Chacon, J.E. & Duong, T. (2013) Data-driven density estimation, with applications to nonparametric clustering and bump hunting. Electronic Journal of Statistics, 7, 499-532.
Comaniciu, D. & Meer, P. (2002). Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 603-619.
library(MASS)
data(crabs)
kms.crabs <- kms(x=crabs[,c("FL","CW")])
plot(kms.crabs)
summary(kms.crabs)
kms.crabs <- kms(x=crabs[,c("FL","CW","RW")])
plot(kms.crabs)
plot(kms.crabs, display="plot3D")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.