specClust() in kknn - arpack iteration limit increase - r

Question

I am applying spectral clustering to a dataset with 4200 rows and 2 columns.
spec <- specClust(df1, centers=7, nn = 7, method = "symmetric")
I have the below error.
n .Call("R_igraph_arpack", func, extra, options, env, sym, PACKAGE = "igraph") :
At arpack.c:944 : ARPACK error, Maximum number of iterations reached
In addition: Warning message:
In .Call("R_igraph_arpack", func, extra, options, env, sym, PACKAGE = "igraph") :
At arpack.c:776 :ARPACK solver failed to converge (1001 iterations, 0/7 eigenvectors converged)
How do i increase the iterations of arpack because this doesnt work:
spec <- specClust(df1, centers=7, nn = 7, method = "symmetric",iter.max=301000)

Answer 1

Digging into the specClust, the ... does not pass anything to the arpack call.
The simplest thing to do I think is to copy the specClust code add maxiter=10000 and source the function in your script.
specCLust2 <- function (data, centers = NULL, nn = 7, method = "symmetric",
gmax = NULL, max.iter = 10000, ...)
{
call = match.call()
if (is.data.frame(data))
data = as.matrix(data)
da = apply(data, 1, paste, collapse = "#")
indUnique = which(!duplicated(da))
indAll = match(da, da[indUnique])
data2 = data
data = data[indUnique, ]
n <- nrow(data)
data = scale(data, FALSE, TRUE)
if (is.null(gmax)) {
if (!is.null(centers))
gmax = centers - 1L
else gmax = 1L
}
test = TRUE
while (test) {
DC = mydist(data, nn)
sif <- rbind(1:n, as.vector(DC[[2]]))
g <- graph(sif, directed = FALSE)
g <- decompose(g, min.vertices = 4)
if (length(g) > 1) {
if (length(g) >= gmax)
nn = nn + 2
else test = FALSE
}
else test = FALSE
}
W <- DC[[1]]
n <- nrow(data)
wi <- W[, nn]
SC <- matrix(1, nrow(W), nn)
SC[] <- wi[DC[[2]]] * wi
W = W^2/SC
alpha = 1/(2 * (nn + 1))
qua = abs(qnorm(alpha))
W = W * qua
W = dnorm(W, sd = 1)
DC[[1]] = W
L = Laplacian(DC, nn, method)
f <- function(x, extra) as.vector(extra %*% x)
if (is.null(centers))
kmax = 25
else kmax = max(centers)
###
#add the maxiter parameter to the arpack call, below
###
U <- arpack(f, extra = L, options = list(n = n, which = "SM",
nev = kmax, ncv = 2 * kmax, mode = 1, maxiter=max.iter), sym = TRUE)
ind <- order(U[[1]])
U[[2]] = U[[2]][indAll, ind]
U[[1]] = U[[1]][ind]
if (is.null(centers)) {
tmp = which.max(diff(U[[1]])) + 1
centers = which.min(AUC(U[[1]][1:tmp]))
}
if (method == "symmetric") {
rs = sqrt(rowSums(U[[2]]^2))
U[[2]] = U[[2]]/rs
}
result = kmeans(U[[2]], centers = centers, nstart = 20, ...)
archeType = getClosest(U[[2]][indAll, ], result$centers)
result$eigenvalue = U[[1]]
result$eigenvector = U[[2]]
result$data = data2
result$indAll = indAll
result$indUnique = indUnique
result$L = L
result$archetype = archeType
result$call = call
class(result) = c("specClust", "kmeans")
result
}