Clustering evaluation through intraclass inertia
Evaluation a clustering algorithm according to intraclass inertia.
intern.intraclass(clus, d, type = c("global", "cluster"))clus |
The extracted clusters. |
d |
The dataset. |
type |
Indicates whether a "global" or a "cluster"-wise evaluation should be used. |
The evaluation of the clustering.
require (datasets) data (iris) km = KMEANS (iris [, -5], k = 3) intern.intraclass (km$clus, iris [, -5])
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.