lcvqe: LCVQE algorithm

In conclust: Pairwise Constraints Clustering

Description

This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.

Usage

lcvqe(data, k, mustLink, cantLink, maxIter = 10)

Arguments

data	The unlabeled dataset.
k	Number of clusters.
mustLink	A list of must-link constraints
cantLink	A list of cannot-link constraints
maxIter	Number of iteration

Details

This algorithm finds a clustering that satisfies as many constraints as possible

Value

A vector that represents the labels (clusters) of the data points

Note

This algorithm can handle noisy constraints.

Author(s)

Tran Khanh Hiep Nguyen Minh Duc

References

Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets

See Also

Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets

Examples

data = matrix(c(0, 1, 1, 0, 0, 0, 1, 1), nrow = 4)
mustLink = matrix(c(1, 2), nrow = 1)
cantLink = matrix(c(1, 4), nrow = 1)
k = 2
pred = lcvqe(data, k, mustLink, cantLink)
pred

Example output

[1] 2 2 1 1

conclust documentation built on May 2, 2019, 1:07 p.m.