Clustering Chain

ClusteringChain{T}(algorithms::Vector{T}) where {T <: AbstractAlgorithm}
ClusteringChain(algorithms::AbstractAlgorithm...)

ClusteringChain represents a chain of clustering algorithms that are executed sequentially. It allows for applying multiple clustering algorithms in a specific order to refine and improve the clustering results.

Type Parameters

• T: algorithm type (concrete for same types, AbstractAlgorithm for mixed types)

Fields

• algorithms: the vector of clustering algorithms that will be executed in sequence.

fit(chain::ClusteringChain, data::AbstractMatrix{<:Real}, k::Integer)

The fit function applies a sequence of clustering algorithms 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()
gmm = GMM(estimator = EmpiricalCovarianceMatrix(n, d))

chain = ClusteringChain(kmeans, gmm)
result = fit(chain, data, k)