clickstream (version 1.1.4)

clusterClickstreams: Performs K-Means Clustering on a List of Clickstreams

Description

Performs k-means clustering on a list of clickstreams. For each clickstream a transition matrix of a given order is computed. These transition matrices are used as input for performing k-means clustering.

Usage

clusterClickstreams(clickstreamList, order = 0, centers, ...)

Arguments

clickstreamList

A list of clickstreams for which the cluster analysis is performed.

order

The order of the transition matrices used as input for clustering (default is 0).

centers

The number of clusters.

...

Additional parameters for k-means clustering (see kmeans).

Value

• This method returns a ClickstreamClusters object (S3-class). It is a list with the following components:
• clusters
  The resulting list of Clickstreams objects.
• centers
  A matrix of cluster centres.
• states
  Vector of states
• totss
  The total sum of squares.
• withinss
  Vector of within- cluster sum of squares, one component per cluster.
• tot.withinss
  Total within-cluster sum of squares, i.e., sum(withinss).
• betweenss
  The between- cluster sum of squares, i.e., totss - tot.withinss.

See Also

print.ClickstreamClusters, summary.ClickstreamClusters

Examples

clickstreams <- c("User1,h,c,c,p,c,h,c,p,p,c,p,p,o",
               "User2,i,c,i,c,c,c,d",
               "User3,h,i,c,i,c,p,c,c,p,c,c,i,d",
               "User4,c,c,p,c,d",
               "User5,h,c,c,p,p,c,p,p,p,i,p,o",
               "User6,i,h,c,c,p,p,c,p,c,d")
csf <- tempfile()
writeLines(clickstreams, csf)
cls <- readClickstreams(csf, header = TRUE)
clusters <- clusterClickstreams(cls, order = 0, centers = 2)
print(clusters)