Loss function for max-margin clustering
mmcLoss(x, k = 3L, minClusterSize = 1L, groups = matrix(logical(0),
nrow(x), 0), minGroupOverlap = matrix(integer(0), k, ncol(groups)),
weight = 1/nrow(x))
numeric matrix representing the dataset (one sample per row)
an integer specifying number of clusters to find
an integer vector specifying the minimum number of sample per cluster. Given values are reclycled if necessary to have one value per cluster.
a logical matrix for instance grouping (groups[i,j] TRUE when sample i belong to group j).
an integer matrix specifyng the minimum number of instance per cluster for each group.
a weight vector for each instance
the loss function to optimize for max margin clustering of the given dataset