Implements a CLARA (Clustering Large Applications) strategy using Jaccard dissimilarity computed on individual patients state matrices. The algorithm repeatedly samples subsets of the data, performs PAM clustering on each subset, and selects the medoids that minimise the total dissimilarity across the full dataset. Final assignments are made by mapping all data points to the nearest selected medoid.
fast_clara_jaccard(
data,
k,
samples = 20,
samplesize = NULL,
seed = 123,
frac = 1
)A list with index of patients from the sample a, medoid indices, cluster assignment, and cost.
An integer vector of cluster assignments for each patient.
Indices of medoids associated witht the lower cost.
Indices of the sampled columns used in clustering.
Total cost (sum of dissimilarities to assigned medoids).
A state matrix of censored time-to-event indicators as computed by the make_state_matrix function.
Number of returned clusters.
Number of random samples drawn from the analysed population.
Number of patients per sample (default: min(50 + 5k, ncol(data))).
Random seed for reproducibility (default: 123).
Fraction of the population to use for cost computation (default: 1).
This implementation adapts the original CLARA method described by Kaufman and Rousseeuw (1990) in "Finding Groups in Data: An Introduction to Cluster Analysis".
Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.