Built-in clustering algorithms

These functions can be used when building a metasnf clustering functions list. Each function converts a similarity matrix (matrix class object) to a cluster solution (numeric vector). Note that these functions (or custom clustering functions) cannot accept number of clusters as a parameter; this value must be built into the function itself if necessary.

Usage

spectral_eigen(similarity_matrix)

spectral_rot(similarity_matrix)

spectral_eigen_classic(similarity_matrix)

spectral_rot_classic(similarity_matrix)

spectral_two(similarity_matrix)

spectral_three(similarity_matrix)

spectral_four(similarity_matrix)

spectral_five(similarity_matrix)

spectral_six(similarity_matrix)

spectral_seven(similarity_matrix)

spectral_eight(similarity_matrix)

spectral_nine(similarity_matrix)

spectral_ten(similarity_matrix)

Arguments

similarity_matrix

A similarity matrix.

Value

solution_data A vector of cluster assignments

Details

• spectral_eigen: Spectral clustering where the number of clusters is based on the eigen-gap heuristic

• spectral_rot: Spectral clustering where the number of clusters is based on the rotation-cost heuristic

• spectral_(C): Spectral clustering for a C-cluster solution.