partitionAgreement: Partition Agreement calculation between two clustering runs

In BioSok/omada: Machine learning tools for automated transcriptome clustering analysis

Partition Agreement calculation between two clustering runs

Description

Calculate the agreement (0,1) between two partitioning generated by two clustering runs using the adjust Rand Index. We can use three clustering algorithms (spectral, kmeans and hierarchical) along with the following parameters for each:

Usage

partitionAgreement(
  data,
  algorithm.1 = "hierarchical",
  measure.1 = "canberra",
  hier.agglo.algorithm.1 = "average",
  algorithm.2 = "hierarchical",
  measure.2 = "manhattan",
  hier.agglo.algorithm.2 = "average",
  number.of.clusters = 5
)

Arguments

data	A dataframe, where columns are features and rows are data points
algorithm.1	Second algorithm to be used (spectral/kmeans/hierarchical)
measure.1	Concerns the first algorithm to be used and represents a kernel for Spectral/kmeans or a distance measure for hierarchical clustering
hier.agglo.algorithm.1	Concerns the first algorithm to be used and represents the agglomerative method for hierarchical clustering (not used in spectral/kmeans clustering)
algorithm.2	First algorithm to be used (spectral/kmeans/hierarchical)
measure.2	Concerns the second algorithm to be used and represents a kernel for Spectral/kmeans or a distance measure for hierarchical clustering
hier.agglo.algorithm.2	Concerns the second algorithm to be used and represents the agglomerative method for hierarchical clustering (not used in spectral/kmeans clustering)
number.of.clusters	The upper limit of clusters to form starting from 2

Details

Spectral kernels: rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot

K-means kernels: "Hartigan-Wong", Lloyd, Forgy, MacQueen

Hierarchical Agglomeration methods: average, ward.D, ward.D2, single, complete, mcquitty, median, centroid

Distance measures: euclidean, manhattan, canberra, minkowski, maximum

Value

An object of class "partitionAgreement" containing agreements (Rand Indexes) from 1 cluster (ARI=0) up to the number of clusters requested

Examples

partitionAgreement(toy_genes, algorithm.1 = "hierarchical",
measure.1 = "canberra",hier.agglo.algorithm.1 = "average",
algorithm.2 = "hierarchical",measure.2 = "manhattan",
hier.agglo.algorithm.2 = "average",number.of.clusters = 3)

partitionAgreement(toy_genes, algorithm.1 = "spectral", measure.1 = "rbfdot",
algorithm.2 = "kmeans",measure.2 = "Lloyd", number.of.clusters = 5)

BioSok/omada documentation built on Aug. 21, 2023, 2:38 p.m.