Runs leveraged affinity propagation clustering
# S4 method for matrix,missing
apclusterL(s, x,
sel, p=NA, q=NA, maxits=1000, convits=100, lam=0.9,
includeSim=FALSE, nonoise=FALSE, seed=NA)
# S4 method for character,ANY
apclusterL(s, x,
frac, sweeps, p=NA, q=NA, maxits=1000, convits=100, lam=0.9,
includeSim=TRUE, nonoise=FALSE, seed=NA, ...)
# S4 method for function,ANY
apclusterL(s, x,
frac, sweeps, p=NA, q=NA, maxits=1000, convits=100, lam=0.9,
includeSim=TRUE, nonoise=FALSE, seed=NA, ...)an \(l \times length(sel)\) similarity
matrix or a similarity function either specified as the name of
a package provided similarity function as character string or a
user provided function object for similarity calculation.
If s is supplied as a similarity matrix, the columns
must correspond to the same sub-selection of samples as
specified in the sel argument and must be in the same
increasing order.
For a package- or user-defined similarity function, additional
parameters can be specified as appropriate for the chosen method
and are passed on to the similarity function via the ...
argument (see below). See the package vignette for a non-trivial
example or supplying a user-defined similarity measure.
input data to be clustered; if x is a matrix or data
frame, rows are interpreted as samples and columns are interpreted
as features; apart from matrices or data frames, x may be
any other structured data type that contains multiple data items -
provided that an appropriate length
function is available that returns the number of items
fraction of samples that should be used for leveraged clustering. The similarity matrix will be generated for all samples against a random fraction of the samples as specified by this parameter.
number of sweeps of leveraged clustering performed with changing randomly selected subset of samples.
selected sample indices; a vector containing the sample indices of the sample subset used for leveraged AP clustering in increasing order.
input preference; can be a vector that specifies
individual preferences for each data point. If scalar,
the same value is used for all data points. If NA,
exemplar preferences are initialized according to the
distribution of non-Inf values in s. How this
is done is controlled by the parameter q. See also
apcluster.
if p=NA, exemplar preferences are initialized
according to the distribution of non-Inf values in s.
If q=NA, exemplar preferences are set to the median
of non-Inf values in s. If q is a value
between 0 and 1, the sample quantile with threshold
q is used, whereas q=0.5 again results in
the median. See also apcluster.
maximal number of iterations that should be executed
the algorithm terminates if the examplars have not
changed for convits iterations
damping factor; should be a value in the range [0.5, 1); higher values correspond to heavy damping which may be needed if oscillations occur
apcluster adds a small amount of noise to
s to prevent degenerate cases; if TRUE,
this is disabled
for reproducibility, the seed of the random number
generator can be set to a fixed value before
adding noise (see above), if NA, the seed remains
unchanged
all other arguments are passed to the selected
similarity function as they are; note that possible name conflicts between
arguments of apcluster and arguments of the similarity
function may occur; therefore, we recommend to write user-defined
similarity functions without additional parameters or to use
closures to fix parameters (such as, in the example below);
Upon successful completion, both functions returns an
object.
Affinity Propagation clusters data using a set of real-valued pairwise similarities as input. Each cluster is represented by a representative cluster center (the so-called exemplar). The method is iterative and searches for clusters maximizing an objective function called net similarity.
Leveraged Affinity Propagation reduces dynamic and static load for large datasets. Only a subset of the samples are considered in the clustering process assuming that they provide already enough information about the cluster structure.
When called with input data and the name of a package provided or a user
provided similarity function the function selects a random sample subset
according to the frac parameter, calculates a rectangular
similarity matrix of all samples against this subset and repeats
affinity propagation sweep times. A new sample subset is used
for each repetition. The clustering result of the sweep with the highest
net similarity is returned. Any parameters specific to the chosen
method of similarity calculation can be passed to apcluster
in addition to the parameters described above. The similarity matrix
for the best trial is also returned in the result object when requested
by the user (argument includeSim).
When called with a rectangular similarity matrix (which represents a
column subset of the full similarity matrix) the function performs
AP clustering on this similarity matrix. The information
about the selected samples is passed to clustering with the
parameter sel. This function is only needed when the user needs full
control of distance calculation or sample subset selection.
Apart from minor adaptations and optimizations, the implementation
of the function apclusterL
is largely analogous to Frey's and Dueck's Matlab code
(see http://www.psi.toronto.edu/affinitypropagation/).
http://www.bioinf.jku.at/software/apcluster
Frey, B. J. and Dueck, D. (2007) Clustering by passing messages between data points. Science 315, 972-976. DOI: 10.1126/science.1136800.
Bodenhofer, U., Kothmeier, A., and Hochreiter, S. (2011) APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463-2464. DOI: 10.1093/bioinformatics/btr406.
, show-methods,
plot-methods, labels-methods,
preferenceRange, apcluster-methods,
apclusterK
## create two Gaussian clouds
cl1 <- cbind(rnorm(150,0.2,0.05),rnorm(150,0.8,0.06))
cl2 <- cbind(rnorm(100,0.7,0.08),rnorm(100,0.3,0.05))
x <- rbind(cl1,cl2)
## leveraged apcluster
apres <- apclusterL(negDistMat(r=2), x, frac=0.2, sweeps=3, p=-0.2)
## show details of leveraged clustering results
show(apres)
## plot leveraged clustering result
plot(apres, x)
## plot heatmap of clustering result
heatmap(apres)
## show net similarities of single sweeps
apres@netsimLev
## show samples on which best sweep was based
apres@sel
Run the code above in your browser using DataLab