This function takes load shape data in a standardized matrix format and creates a dictionary of resulting cluster centers
1 2 3 | create_dictionary(sdata, target.size = 1000, mode = 1, d.metric = 1,
ths = 0.2, iter.max = 100, nstart = 1, two.step.compress = F,
verbose = F)
|
sdata |
source data, assume it's already standardized (cleansed and n by p matrix format) |
target.size |
target size of the dictionary (i.e. nubmer of clusters) |
mode |
1: use ths1, 2: use ths2, 3: use ths3, 4: use ths4 |
d.metric |
1: use euclidean distance metric, otherwise use cosine distance metric |
ths |
will be transferred to akmeans parameter according to mode setting
|
iter.max |
maximum iteration setting to be used in kmeans |
two.step.compress |
whether to reduce the dictionary only by hierarchical clustering or hier+use top N shapes. this option gets activated only when the ratio (original dictionary size before compression/target.size) is larger than 10 |
verbose |
whether to show log or not |
n.start |
parameter to be transferred to kmeans |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.