create_dictionary: Perform load shape clusing and return cluster centers

In ConvergenceDA/visdomloadshape: R package for load shape clustering and analysis, extending the visdom package

Description

This function takes load shape data in a standardized matrix format and creates a dictionary of resulting cluster centers

Usage

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)

Arguments

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 ths1: threshold to decide whether to increase k or not: check sum((sample-assigned center)^2) < ths1*sum(assigned center^2) ths2: threshold to decide whether to increase k or not: check all components of |sample-assigned center| < ths2 ths3: threshold to decide whether to increase k or not: check inner product of (sample,assigned center) > ths3 , this is only for cosine distance metric ths4: threshold to decide whether to increase k or not: check sum(abs(sample-assigned center)) < ths4
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

ConvergenceDA/visdomloadshape documentation built on May 8, 2019, 8:34 a.m.