setcover: greedy setcover optimisation

In extracat: Categorical Data Analysis and Visualization

Description

This function takes an indicator matrix with rows representing objects and columns representing sets and computes a minimal redundancy free set using the greedy setcover optimization algorithm. The aim is to find a minimal set of clusters which covers all objects (or a minimum proportion rat).

Alternatively the number of clusters k can be specified. Then the problem becomes a maximum covergae problem. Both versions also permit weights such as frequencies (weighted setcover/maximum coverage).

Usage

setcover(x, k = NULL, rat = 1, s = NULL, w = NULL, check = TRUE)

Arguments

x	The indicator matrix.
k	An optional number of clusters.
rat	The minimum proportion of objects that is to be covered by the cluster set. If weights are specified in w then those are respected.
s	If weights are specified but not all objects are covered by one of the sets it can be necessary to specify the total weight in order to compute a sensible ratio.
w	Optional weights per object.
check	Whether or not to check for redundancies.

Value

The indices of the clusters in the minimal redundancy-free set. The result is not always the globally optiomal solution since the algorithm is greedy.

Note

This is written supporting the GSAC algorithm.

Author(s)

Alexander Pilhoefer

See Also

gsac

Examples

# compute 100 clusterings with 24 clusters each:
sc <- scale(olives[,3:10])
km100 <- as.data.frame(replicate(100, kmeans(sc,centers = 24)$cluster))

# convert to indicator matrix
I100 <- idat(km100)

# select from all clusters a minimum set:
scover <- setcover(as.matrix(I100))


cdata <- subtable(
	as.data.frame(cbind(olives[,1:2],
	I100[,scover])),1:(length(scover)+2))
scpcp(cdata,sel="Area")

extracat documentation built on July 17, 2018, 5:05 p.m.