 Pan - 3 years ago 275
R Question

# Evaluate the optimum number of clusters “k” in time-series clustering using dtwclust package

I use following tsclust statement to cluster data

``````SURFSKINTEMP_CLUST <- tsclust(SURFSKINTEMP, k = 10L:20L,
distance = "dtw_basic", centroid = "dba",
trace = TRUE, seed = 938,
norm = "L2", window.size = 2L,
args = tsclust_args(cent = list(trace = TRUE)))
``````

SURFSKINTEMP is very big,

``````str(SURFSKINTEMP)
List of 327239
\$ V1     : num [1:7] 0.13 0.631 -0.178 0.731 0.86 ...
\$ V2     : num [1:6] 0.117 -0.693 -0.911 -0.911 -0.781 ...
\$ V3     : num [1:7] 0.117 -0.693 -0.911 -0.911 -0.781 ...
\$ V4     : num [1:6] -0.693 -0.911 -0.911 -0.781 -0.604 ...
``````

Then, I want use cvi to evaluate the optimum number of clusters “k”

``````names(SURFSKINTEMP_CLUST) <- paste0("k_",10L:20L)
sapply(SURFSKINTEMP_CLUST, cvi, type = "internal")
``````

But, there have an errors

``````> sapply(SURFSKINTEMP_CLUST, cvi, type = "internal")
Error: cannot allocate vector of size 797.8 Gb
``````

How can I evaluate the optimum number of clusters “k” in my case? Alexis
Specifying `type = "internal"` will try to calculate 7 indices: Silhouette, Dunn, COP, DB, DB*, CH and SF. As mentioned in the documentation for `cvi`, the first 3 will try to calculate the whole cross-distance matrix, which in your case would be a `327,239 x 327,239` matrix; you're going to have a hard time finding a computer that can allocate that, and it would take a long time to compute.
``````sapply(SURFSKINTEMP_CLUST, cvi, type = c("DB", "DBstar"))
You could also look at the somewhat simple elbow method bearing in mind that you could calculate the sum of squared error (SSE) with (see documentation for `TSClusters-class`):
``````sapply(SURFSKINTEMP_CLUST, function(cl) { sum(cl@cldist ^ 2) })