traj (version 3.1.0)

trajReduce: Select a Subset of the Measures Using a Similarity Index on the Set of Clusterings

Description

This function examines the effect of reducing the number of measures on which the trajectories are clustered. Specifically, starting from a clustering \(C\) in the form of an object of class trajClusters and a choice of a similarity index to compare clusterings, this function finds the subset of measures which results in the clustering most similar to \(C\).

Usage

trajReduce(Measures, Clusters, index = "ARI", keep = 3)

Arguments

Measures

object of class trajMeasures as returned by trajMeasures.

Clusters

object of class trajClusters as returned by trajClusters.

index

The similarity index. Either "ARI" for the Adjusted Rand Index of Hubert and Arabie (1985), "nVId" for the normalized variation of information distance (eg. Meila (2007)) or "nSJd" for the normalized split/joint distance of van Dongen (2000).

keep

The number of measures to keep. Defaults to 3.

Details

The Rand index ranges from 0 to 1 with 0 indicating identical clusters and 1 indicating maximally different clusters. The normalized variation of information distance (nVId) and normalized split-join distance (nSJd) and have the opposite interpretation with 0 indicating maximally different clusters and 1 indicating identical clusters. Therefor, to facilitate comparison, we plot 1 - nVId (resp. 1 - nSJd) instead of nVId (resp. nSJd).

The function runs the trajClusters function on every combination of keep measures and identifies the reduced representation yielding the clustering most similar to the (hard) clustering in Clusters. If the (hard) clustering in Clusters derives from a soft clustering, the iterations of trajClusters will also be ran using fuzzy = TRUE and the corresponding hard clusterings will be used for comparison.

References

Hubert L, Arabie P. Comparing partitions. Journal of Classification 2:193-218, 1985.

Meila M. Comparing clusterings -- an information based distance. Journal of Multivariate Analysis, 98, pp 873-895, 2007.

van Dongen S. Performance criteria for graph clustering and Markov cluster experiments. Technical Report INS-R0012, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam, May 2000.

Examples

if (FALSE) {
data("trajdata")
trajdata.noGrp <- trajdata[, -which(colnames(trajdata) == "Group")] #remove the Group column

m = trajMeasures(trajdata.noGrp, ID = TRUE, measures = 1:20)
s2.4 <- trajClusters(m, nclusters = 4)
trajReduce(m, s2.4)
}