Description Usage Arguments References See Also Examples
This function estimates the best k value for the number of partitions the dataset should be segmented.
1 | gama.how.many.k(dataset = NULL, method = "minimal")
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dataset |
the original dataset used for clustering. |
method |
the method used to estimate the number of partitions. If 'minimal' is used, the function will perform estimation based on finding the 'elbow' in the Within-cluster Sum of Squares Error graphic. It uses a second derivative approximation, in order to suggest k. If 'broad' is used, the function will proceed an estimation by majority voting of 24 indices, by using the NbClust package. |
Malika Charrad, Nadia Ghazzali, Veronique Boiteau, Azam Niknafs (2014). NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. Journal of Statistical Software, 61(6), 1-36. URL http://www.jstatsoft.org/v61/i06/.
gama
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1 2 3 4 5 6 7 8 9 10 11 12 | # loads data about CPU execution metrics of a distributed
# version of Alternating Least Squares (ALS) algorithm
library(gama)
data(cpu.als)
# call estimation by using minimal method (Elbow graphic)
k <- gama.how.many.k (cpu.als)
print(k)
# call estimation by using broad method (NbClust)
k <- gama.how.many.k (cpu.als, method = 'broad')
print(k)
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