UAHDataScienceUC (version 1.0.1)

genetic_kmeans: Genetic K-Means Clustering

Description

Performs Genetic K-Means clustering on a data matrix.

Usage

genetic_kmeans(
  data,
  k,
  population_size = 10,
  mut_probability = 0.5,
  max_generations = 10,
  learn = FALSE,
  waiting = TRUE,
  ...
)

Value

A kmeans object as returned by the original kmeans function.

Arguments

data

a set of observations, presented as a matrix-like object where every row is a new observation.

k

the number of clusters.

population_size

the number of individuals in the population.

mut_probability

the probability of a mutation occurring.

max_generations

the maximum number of iterations allowed.

learn

a Boolean determining whether intermediate logs explaining how the algorithm works should be printed or not.

waiting

a Boolean determining whether the intermediate logs should be printed in chunks waiting for user input before printing the next or not.

...

additional arguments passed to proxy::dist().

Author

Eduardo Ruiz Sabajanes,

Examples

### Example 1: Simple usage with circles dataset
result1 <- genetic_kmeans(db1[1:20,], 2, learn = TRUE, waiting = FALSE)

### Example 2: Moons dataset with different population size
result2 <- genetic_kmeans(db2[1:20,], 2, population_size = 20,
                         learn = TRUE, waiting = FALSE)

### Example 3: Varying density clusters with different mutation probability
result3 <- genetic_kmeans(db3[1:20,], 3, mut_probability = 0.7,
                         learn = TRUE, waiting = FALSE)

### Example 4: Well-separated clusters with larger population
result5 <- genetic_kmeans(db5[1:20,], 3, population_size = 30,
                         mut_probability = 0.6, learn = TRUE, waiting = FALSE)

### Example 5: Using different parameters combinations
result6 <- genetic_kmeans(db1[1:20,], 2,
                         population_size = 15,
                         mut_probability = 0.8,
                         max_generations = 15,
                         learn = TRUE,
                         waiting = FALSE)