Multi-Start

MultiStart(
    local_search::AbstractAlgorithm
    verbose::Bool = DEFAULT_VERBOSE
    max_iterations::Integer = 200
)

The MultiStart approach repeatedly applies a clustering algorithm to generate multiple solutions with different initial points and selects the best solution.

Fields

• local_search: the clustering algorithm applied to improve the solution in each meta-heuristics iteration.
• verbose: controls whether the algorithm should display additional information during execution.
• max_iterations: represents the maximum number of iterations the algorithm will perform before stopping.

fit(
    meta::MultiStart,
    data::AbstractMatrix{<:Real},
    k::Integer
)

The fit function applies a multi-start to a clustering problem and returns a result object representing the clustering outcome.

Parameters:

• meta: an instance representing the clustering settings and parameters.
• data: a floating-point matrix, where each row represents a data point, and each column represents a feature.
• k: an integer representing the number of clusters.

Example

n = 100
d = 2
k = 2

data = rand(n, d)

kmeans = Kmeans()
multi_start = MultiStart(local_search = kmeans)
result = fit(multi_start, data, k)