parallelpam - Parallel Partitioning-Around-Medoids (PAM) for Big Sets of Data
Application of the Partitioning-Around-Medoids (PAM)
clustering algorithm described in Schubert, E. and Rousseeuw,
P.J.: "Fast and eager k-medoids clustering: O(k) runtime
improvement of the PAM, CLARA, and CLARANS algorithms."
Information Systems, vol. 101, p. 101804, (2021).
<doi:10.1016/j.is.2021.101804>. It uses a binary format for
storing and retrieval of matrices developed for the 'jmatrix'
package but the functionality of 'jmatrix' is included here, so
you do not need to install it. Also, it is used by package
'scellpam', so if you have installed it, you do not need to
install this package. PAM can be applied to sets of data whose
dissimilarity matrix can be very big. It has been tested with
up to 100.000 points. It does this with the help of the code
developed for other package, 'jmatrix', which allows the matrix
not to be loaded in 'R' memory (which would force it to be of
double type) but it gets from disk, which allows using float
(or even smaller data types). Moreover, the dissimilarity
matrix is calculated in parallel if the computer has several
cores so it can open many threads. The initial part of the PAM
algorithm can be done with the BUILD or LAB algorithms; the
BUILD algorithm has been implemented in parallel. The
optimization phase implements the FastPAM1 algorithm, also in
parallel. Finally, calculation of silhouette is available and
also implemented in parallel.
