cluster(x, newdata, ...)
tNNobject. Note that this function canges the original object!
cluster()implements tNN clustering The dissimilarity between the new observation and the centers of the clusters is calculated. The new observation is assigned to the closest cluster if the dissimilarity value is smaller than the threshold (for the state). If no such state exists, a new state is created for the observation. This simple clustering algorithm is called nearest neighbor threshold nearest neighbor (threshold NN).
NAs are handled in the data by using only the other
dimensions if the data for dissimilarity computation
The clusters which the data points in the last
operation where assigned to can be retrieved using the method
## load EMMTraffic data data(EMMTraffic) ## create empty clustering tnn <- tNN(th=0.2, measure="eJaccard") tnn ## cluster some data cluster(tnn, EMMTraffic) tnn ## what clusters were the data points assigned to? last_clustering(tnn) ## plot the clustering as a scatterplot matrix of the cluster centers plot(tnn)