Data: [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.
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: Data 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.
Samples: Integer, say N, the number of samples to be drawn from the dataset. Default value set as recommended by documentation of clara
Random: Logical indicating if R's random number generator should be used instead of the primitive clara()-builtin one.
...: Further arguments to be set for the clustering algorithm, if not set, default arguments are used.
Details
It is recommended to use set.seed if clustering output should be always the same instead of setting Random=FALSE in order to use the primitive clara()-builtin random number generator.
Returns
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
[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.