KMedoids: KMedoids Class

In banditpam: Almost Linear-Time k-Medoids Clustering

KMedoids Class

Description

This class wraps around the C++ KMedoids class and exposes methods and fields of the C++ object.

Active bindings

k

(integer(1))
The number of medoids/clusters to create

max_iter

(integer(1))
max_iter the maximum number of SWAP steps the algorithm runs

build_conf

(integer(1))
Parameter that affects the width of BUILD confidence intervals, default 1000

swap_conf

(integer(1))
Parameter that affects the width of SWAP confidence intervals, default 10000

loss_fn

(character(1))
The loss function, "lp" (for p integer > 0) or one of "manhattan", "cosine", "inf" or "euclidean"

Methods

Public methods

• KMedoids$new()

• KMedoids$get_algorithm()

• KMedoids$fit()

• KMedoids$get_medoids_final()

• KMedoids$get_labels()

• KMedoids$get_statistic()

• KMedoids$print()

• KMedoids$clone()

---

Method new()

Create a new KMedoids object

Usage

KMedoids$new(
  k = 5L,
  algorithm = c("BanditPAM", "PAM", "FastPAM1"),
  max_iter = 1000L,
  build_conf = 1000,
  swap_conf = 10000L
)

Arguments

k

number of medoids/clusters to create, default 5

algorithm

the algorithm to use, one of "BanditPAM", "PAM", "FastPAM1"

max_iter

the maximum number of SWAP steps the algorithm runs, default 1000

build_conf

parameter that affects the width of BUILD confidence intervals, default 1000

swap_conf

parameter that affects the width of SWAP confidence intervals, default 10000

Returns

a KMedoids object which can be used to fit the banditpam algorithm to data

---

Method get_algorithm()

Return the algorithm used

Usage

KMedoids$get_algorithm()

Returns

a string indicating the algorithm

---

Method fit()

Fit the KMedoids algorthm given the data and loss. It is advisable to set the seed before calling this method for reproducible results.

Usage

KMedoids$fit(data, loss, dist_mat = NULL)

Arguments

data

the data matrix

loss

the loss function, either "lp" (p, integer indicating L_p loss) or one of "manhattan", "cosine", "inf" or "euclidean"

dist_mat

an optional distance matrix

---

Method get_medoids_final()

Return the final medoid indices after clustering

Usage

KMedoids$get_medoids_final()

Returns

a vector indices of the final mediods

---

Method get_labels()

Return the cluster labels after clustering

Usage

KMedoids$get_labels()

Returns

a vector of the cluster labels for the observations

---

Method get_statistic()

Get the specified statistic after clustering

Usage

KMedoids$get_statistic(what)

Arguments

what

a string which should one of "dist_computations", "dist_computations_and_misc", "misc_dist", "build_dist", "swap_dist", "cache_writes", "cache_hits", or "cache_misses"

return

the statistic

---

Method print()

Printer.

Usage

KMedoids$print(...)

Arguments

...

(ignored).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

KMedoids$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Generate data from a Gaussian Mixture Model with the given means:
set.seed(10)
n_per_cluster <- 40
means <- list(c(0, 0), c(-5, 5), c(5, 5))
X <- do.call(rbind, lapply(means, MASS::mvrnorm, n = n_per_cluster, Sigma = diag(2)))
obj <- KMedoids$new(k = 3)
obj$fit(data = X, loss = "l2")
meds <- obj$get_medoids_final()
plot(X[, 1], X[, 2])
points(X[meds, 1], X[meds, 2], col = "red", pch = 19)

banditpam documentation built on June 8, 2025, 1:39 p.m.