+#' @name clustering
+#' @rdname clustering
+#' @aliases clusteringTask1 computeClusters1 computeClusters2
+#'
+#' @title Two-stage clustering, withing one task (see \code{claws()})
+#'
+#' @description \code{clusteringTask1()} runs one full stage-1 task, which consists in
+#' iterated stage 1 clustering (on nb_curves / ntasks energy contributions, computed
+#' through discrete wavelets coefficients).
+#' \code{clusteringTask2()} runs a full stage-2 task, which consists in synchrones
+#' and then WER distances computations, before applying the clustering algorithm.
+#' \code{computeClusters1()} and \code{computeClusters2()} correspond to the atomic
+#' clustering procedures respectively for stage 1 and 2. The former applies the
+#' clustering algorithm (PAM) on a contributions matrix, while the latter clusters
+#' a chunk of series inside one task (~max nb_series_per_chunk)
+#'
+#' @param indices Range of series indices to cluster in parallel (initial data)
+#' @param getContribs Function to retrieve contributions from initial series indices:
+#' \code{getContribs(indices)} outpus a contributions matrix
+#' @param contribs matrix of contributions (e.g. output of \code{curvesToContribs()})
+#' @inheritParams computeSynchrones
+#' @inheritParams claws
+#'
+#' @return For \code{clusteringTask1()} and \code{computeClusters1()}, the indices of the
+#' computed (K1) medoids. Indices are irrelevant for stage 2 clustering, thus
+#' \code{computeClusters2()} outputs a big.matrix of medoids
+#' (of size limited by nb_series_per_chunk)
+NULL
+
+#' @rdname clustering
+#' @export
+clusteringTask1 = function(
+ indices, getContribs, K1, nb_series_per_chunk, ncores_clust=1, verbose=FALSE, parll=TRUE)