clue (version 0.3-65)

partition: Partitions

Description

Determine whether an R object represents a partition of objects, or coerce to an R object representing such.

Usage

is.cl_partition(x)
is.cl_hard_partition(x)
is.cl_soft_partition(x)
as.cl_partition(x)
as.cl_hard_partition(x)

Value

For the testing functions, a logical indicating whether the given object represents a clustering of objects of the respective kind.

For the coercion functions, a container object inheriting from

"cl_partition", with a suitable representation of the partition given by x.

Arguments

x

an R object.

Details

is.cl_partition and is.cl_hard_partition are generic functions.

The methods provided in package clue handle the partitions obtained from clustering functions in the base R distribution, as well as packages RWeka, cba, cclust, cluster, e1071, flexclust, flexmix, kernlab, mclust, movMF and skmeans (and of course, clue itself).

is.cl_soft_partition gives true iff is.cl_partition is true and is.cl_hard_partition is false.

as.cl_partition returns an object of class "cl_partition" “containing” the given object x if this already represents a partition (i.e., is.cl_partition(x) is true), or the memberships obtained from x via as.cl_membership.

as.cl_hard_partition(x) returns an object which has class "cl_hard_partition" and inherits from "cl_partition", and contains x if it already represents a hard partition (i.e., provided that is.cl_hard_partition(x) is true), or the class ids obtained from x, using x if this is an atomic vector of raw class ids, or, if x represents a soft partition or is a raw matrix of membership values, using the class ids of the nearest hard partition, defined by taking the class ids of the (first) maximal membership values.

Conceptually, partitions and hard partitions are virtual classes, allowing for a variety of representations.

There are group methods for comparing partitions and computing their minimum, maximum, and range based on the meet and join operations, see cl_meet.

Examples

data("Cassini")
pcl <- kmeans(Cassini$x, 3)
is.cl_partition(pcl)
is.cl_hard_partition(pcl)
is.cl_soft_partition(pcl)