lcvqe {conclust} R Documentation

## LCVQE algorithm

### 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

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