idx = cluster(obj,X)
[idx,nlogl] = cluster(obj,X)
[idx,nlogl,P] = cluster(obj,X)
[idx,nlogl,P,logpdf] = cluster(obj,X)
[idx,nlogl,P,logpdf,M] = cluster(obj,X)
idx = cluster(obj,X)
partitions data in
the n-by-d matrix X
,
where n is the number of observations and d is
the dimension of the data, into k clusters determined
by the k components of the Gaussian mixture distribution
defined by obj
. obj
is an object
created by gmdistribution
or fitgmdist
. idx
is an n-by-1
vector, where idx(I)
is the cluster index of observation I
.
The cluster index gives the component with the largest posterior probability
for the observation, weighted by the component probability.
Note:
The data in |
cluster
treats NaN
values
as missing data. Rows of X
with NaN
values
are excluded from the partition.
[idx,nlogl] = cluster(obj,X)
also returns nlogl
,
the negative log-likelihood of the data.
[idx,nlogl,P] = cluster(obj,X)
also returns
the posterior probabilities of each component for each observation
in the n-by-k matrix P
. P(I,J)
is
the probability of component J
given observation I
.
[idx,nlogl,P,logpdf] = cluster(obj,X)
also
returns the n-by-1 vector logpdf
containing
the logarithm of the estimated probability density function for each
observation. The density estimate for observation I
is
a sum over all components of the component density at I
times
the component probability.
[idx,nlogl,P,logpdf,M] = cluster(obj,X)
also
returns an n-by-k matrix M
containing
Mahalanobis distances in squared units. M(I,J)
is
the Mahalanobis distance of observation I
from
the mean of component J
.