modelInfo <- list(label = "Bagged Clustering",
"library" = c("e1071"),
type = c('Classification'),
parameters = data.frame(parameter = c('centers', 'dist.method' ),
class = c('integer', 'character'),
label = c('#Number of clusters, k',
'Distance method')),
grid = function(x, len = NULL) {
p <- ncol(x)
n <- nrow(x)
distMethods <- c("euclidian", "maximum", "manhattan", "canberra")
kmax <- max(c(3, 2 + len))
iter.base <- c(10)
minsizeMax <- max(c(10, n/(kmax^2)))
k <- ceiling(seq(2, kmax, length.out = len))
minsize <- ceiling(seq(0, minsizeMax, length.out = len))
if(len == 1){
expand.grid(centers = 2, dist.method = "euclidian")
} else{
expand.grid(centers = k, dist.method = "euclidian")
}
},
fit = function(x, wts, lev, param, last, classProbs, ...) {
theDots <- list(...)
bclust(x = x, centers = param$centers,
dist.method = param$dist.method, ...)
},
predict = function(modelFit, newdata, submodels = NULL, ...){
theDots <- list(...)
bclust(x = newdata,
centers = length(unique(modelFit$cluster)),
dist.method = modelFit$dist.method, ...)$cluster
},
prob = function(modelFit, newdata, submodels = NULL)
stop("Not written"),
varImp = function(object, ...) {
stop("Not written")
},
tags = c("Hierarchical"),
sort = function(x) x[order(x$centers),])
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