flowclusterTimeseries: Cluster a processed time-series with 'flowClust' &...
In segmenTier: Similarity-Based Segmentation of Multidimensional Signals

A wrapper for flowClust, clustering a time-series object tset provided by processTimeseries, where specifically the DFT of a time-series and requested data transformation were calculated. This is intended to work in the same way as clusterTimeseries but was so far only tested for clustering of the final segment time-series, as previously applied to microarray data from yeast by Machne & Murray (2012) <doi:10.1371/journal.pone.0037906> and from cyanobacteria by Lehmann et al. (2013) <doi:10.1186/1471-2105-14-133>. It could in principle also be used for segmentation, but that has not been extensively tested. flowClust implements a model-based clustering approach and is much slower then kmeans used in clusterTimeseries. Please see option ncpu on how to use parallel mode, which does not work on some installations. However, model-based clustering has the advantage of an intrinsic measure (BIC) to decide on the optimal cluster numbers. Additionally, the clusters can be "merged" to fewer clusters at constant BIC using flowMerge.

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flowclusterTimeseries(tset, ncpu = 1, K = 10, selected,
  merge = FALSE, B = 500, tol = 1e-05, lambda = 1, nu = 4,
  nu.est = 0, trans = 1, ...)

`tset`	processed time-series as provided by `processTimeseries`
`ncpu`	number of cores available for parallel mode of flowClust. NOTE: parallel mode of `flowClust` is often non-functional. Alternatively, you can set `options(mc.cores=ncpu)` directly.
`K`	the requested cluster numbers (vector of integers)
`selected`	a pre-selected cluster number which is then used as a start clustering for `flowMerge` (if option `merge==TRUE`)
`merge`	logical indicating whether cluster merging with `flowMerge` should be attempted
`B`	maximal number of EM iterations
`tol`	tolerance for EM convergence
`lambda`	initial Box-Cox trafo
`nu`	degrees of freedom used for the t distribution, Inf for pure Gaussian
`nu.est`	0: no, 1: non-specific, 2: cluster-specific estimation of nu
`trans`	0: no, 1: non-specific, 2: cluster-specific estim. of lambda
`...`	further parameters for `flowClust`