Some simple tools for clustering
devtools::install_github("IsaacShaffer/clusterTools")
For spectral clustering with radial kernel, the main function is:
iris_demo <- SPECC_RK(RADIAL_KERNEL(DIST(as.matrix(iris[,1:4]))),
3, 70)The results for such a use look like:
> iris_demo
K-means clustering with 3 clusters of sizes 50, 48, 52
Cluster means:
[,1] [,2] [,3]
1 0.005038687 0.11343578 0.08164966
2 0.088160471 -0.05719074 0.08164966
3 -0.085003231 -0.04905044 0.08164966
Clustering vector:
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[48] 1 1 1 3 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 2 2 3 2 2 3 2 2 2 2 2 2 2
[95] 2 2 2 2 2 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
[142] 3 3 3 3 3 3 3 3 3
Within cluster sum of squares by cluster:
[1] 0.03341224 0.13228867 0.15874168
(between_SS / total_SS = 83.8 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss"
[7] "size" "iter" "ifault"
They can be used in conjunction with other clustering assessment tools like pdfCluster::adj.rand.index:
require(pdfCluster)
pdfCluster::adj.rand.index(iris_demo$cluster, iris[,5])
[1] 0.8176016
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