PAMClustering: Partitioning Around Medoids (PAM)

In Mthrun/FCPS: Fundamental Clustering Problems Suite

Partitioning Around Medoids (PAM)

Description

Partitioning (clustering) of the data into k clusters around medoids, a more robust version of k-means [Rousseeuw/Kaufman, 1990, p. 68-125] .

Usage

PAMclustering(DataOrDistances,ClusterNo,

PlotIt=FALSE,Standardization=TRUE,Data,...)

Arguments

DataOrDistances	[1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features. Alternatively, symmetric [1:n,1:n] distance matrix
ClusterNo	A number k which defines k different clusters to be built by the algorithm.
PlotIt	Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
Standardization	DataOrDistances is standardized before calculating the dissimilarities. Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's mean absolute deviation.If DataOrDistances is already a distance matrix, then this argument will be ignored.
Data	[1:n,1:d] data matrix in the case that DataOrDistances is missing and partial matching does not work.
...	Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

[Rousseeuw/Kaufman, 1990, chapter 2] or [Reynolds et al., 1992].

Value

List of

Cls	[1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering.
Object	Object defined by clustering algorithm as the other output of this algorithm

Author(s)

Michael Thrun

References

[Rousseeuw/Kaufman, 1990] Rousseeuw, P. J., & Kaufman, L.: Finding groups in data, Belgium, John Wiley & Sons Inc., ISBN: 0471735787, doi:10.1002/9780470316801, Online ISBN: 9780470316801, 1990.

[Reynolds et al., 1992] Reynolds, A., Richards, G.,de la Iglesia, B. and Rayward-Smith, V.: Clustering rules: A comparison of partitioning and hierarchical clustering algorithms, Journal of Mathematical Modelling and Algorithms 5, 475-504, DOI:10.1007/s10852-005-9022-1, 1992.

Examples

data('Hepta')
out=PAMclustering(Hepta$Data,ClusterNo=7,PlotIt=FALSE)

Mthrun/FCPS documentation built on June 28, 2023, 9:29 a.m.