% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kmeans_smote_impl.R
\name{kmeans_smote}
\alias{kmeans_smote}
\title{KMeans-SMOTE Algorithm}
\usage{
kmeans_smote(
  df,
  var,
  k = 2,
  over_ratio = 1,
  num_clusters = NULL,
  cluster_balance_threshold = 1,
  density_exponent = NULL,
  distance = "euclidean"
)
}
\arguments{
\item{df}{data.frame or tibble. Must have 1 factor variable and remaining
numeric variables.}

\item{var}{Character, name of variable containing factor variable.}

\item{k}{An integer. Number of nearest neighbor that are used
to generate the new examples of the minority class.}

\item{over_ratio}{A numeric value for the ratio of the
minority-to-majority frequencies. The default value (1) means
that all other levels are sampled up to have the same
frequency as the most occurring level. A value of 0.5 would mean
that the minority levels will have (at most) (approximately)
half as many rows as the majority level.

A named numeric vector can be used instead to give different levels
different targets, for example \code{c(a = 1, b = 0.5)}. The names must be
levels of the outcome and the values are ratios of the majority level,
exactly as in the single-number case. Levels that are not named are left
untouched, as are rows with a missing outcome. Because a vector of targets
is not a single value, supplying one means this argument can no longer be
tuned. See \code{vignette("ratio", package = "themis")} for more details.}

\item{num_clusters}{An integer, the number of clusters to split the predictor
space into, or \code{NULL} (the default) to use \code{max(2, floor(sqrt(n / 2)))}
where \code{n} is the number of observations.}

\item{cluster_balance_threshold}{A number. A cluster is used for
over-sampling a class only if it contains at least
\code{cluster_balance_threshold} times as many observations of that class as of
all other classes combined. Defaults to \code{1}, meaning that the class must be
at least as common as the rest of the data within the cluster. Smaller
values keep more clusters.}

\item{density_exponent}{A number, the exponent applied to the average
pairwise distance when measuring how sparse a cluster is, or \code{NULL} (the
default) to use the number of predictors.}

\item{distance}{A character string specifying the distance metric used for
nearest neighbor calculations, defaulting to \code{"euclidean"}. The available
metrics fall into three groups.

\code{"euclidean"}, \code{"cosine"}, and \code{"mahalanobis"} use approximate nearest
neighbors via the RANN package and scale well to large datasets.

\code{"squared_chord"}, \code{"matusita"}, \code{"hellinger"}, and \code{"bhattacharyya"} are
probability-divergence measures that treat each row as a distribution over
the predictors, so they require non-negative values. \code{"hellinger"} and
\code{"bhattacharyya"} further require each row to sum to 1. All four also use
the RANN package and scale well to large datasets.

\code{"manhattan"}, \code{"chebyshev"}, \code{"canberra"}, \code{"soergel"}, \code{"lorentzian"},
\code{"jeffreys"}, \code{"topsoe"}, \code{"jensen-shannon"}, \code{"jensen_difference"},
\code{"taneja"}, and \code{"kumar-johnson"} compute an exact all-pairs distance
matrix. This takes time and memory proportional to the square of the number
of observations in a class, so these are best suited to smaller datasets.
Everything from \code{"canberra"} onwards is a probability divergence requiring
non-negative values, is provided by the philentropy package (which must be
installed separately), and in the case of \code{"jeffreys"}, \code{"taneja"}, and
\code{"kumar-johnson"} requires strictly positive values, since those divide by
individual predictor values.

The probability divergences are meaningful for compositional predictors such
as proportions or counts normalized per observation, and are generally not
appropriate for standardized predictors.}
}
\value{
A data.frame or tibble, depending on type of \code{df}.
}
\description{
KMeans-SMOTE clusters the predictor space, keeps only the clusters that are
dominated by the class being over-sampled, and generates new examples with
SMOTE inside those clusters, giving sparser clusters more of the new points.
}
\details{
KMeans-SMOTE combines k-means clustering with SMOTE to avoid generating
synthetic points in regions where the minority class is not actually
present. It works in three stages:
\enumerate{
\item The predictor space of the whole data set is clustered with
\code{\link[stats:kmeans]{stats::kmeans()}} into \code{num_clusters} clusters.
\item Each cluster is either kept or filtered out. A cluster is kept for a
given minority class if the ratio of that class's observations to all
other observations in the cluster is at least
\code{cluster_balance_threshold}, and if the cluster contains more than
\code{neighbors} observations of that class so that interpolation is
possible.
\item The synthetic points are distributed over the kept clusters according to
how sparse each cluster is. The sparsity of a cluster is
\code{mean_pairwise_distance ^ density_exponent / n}, where \code{n} is the number
of minority observations in the cluster. Sparser clusters receive more
points. Within each cluster the points are then generated by ordinary
SMOTE interpolation, using only that cluster's observations as
neighbors.
}

Filtering on cluster balance keeps synthetic points away from
majority-dominated regions, and the density weighting counteracts
within-class imbalance rather than only between-class imbalance.

The clustering is done once on all observations, so in a multi-class
problem every minority class shares the same clusters; only the filtering
and weighting are done per class. Note also that k-means always uses
Euclidean distance. The \code{distance} argument affects the nearest-neighbor
search used for interpolation and the sparsity calculation, not the
clustering itself.

All columns used in this function must be numeric with no missing data.
}
\examples{
circle_numeric <- circle_example[, c("x", "y", "class")]

res <- kmeans_smote(circle_numeric, var = "class")

res <- kmeans_smote(circle_numeric, var = "class", num_clusters = 10)

res <- kmeans_smote(circle_numeric, var = "class", over_ratio = 0.8)
}
\references{
Douzas, G., Bacao, F., and Last, F. (2018). Improving imbalanced
learning through a heuristic oversampling method based on k-means and SMOTE.
Information Sciences, 465:1-20.
}
\seealso{
\code{\link[=step_kmeans_smote]{step_kmeans_smote()}} for step function of this method

Other Direct Implementations:
\code{\link[=adasyn]{adasyn()}},
\code{\link[=bsmote]{bsmote()}},
\code{\link[=cluster_centroids]{cluster_centroids()}},
\code{\link[=cnn]{cnn()}},
\code{\link[=enn]{enn()}},
\code{\link[=instance_hardness]{instance_hardness()}},
\code{\link[=ncl]{ncl()}},
\code{\link[=nearmiss]{nearmiss()}},
\code{\link[=oss]{oss()}},
\code{\link[=rose]{rose()}},
\code{\link[=smogn]{smogn()}},
\code{\link[=smote]{smote()}},
\code{\link[=smoten]{smoten()}},
\code{\link[=smotenc]{smotenc()}},
\code{\link[=svmsmote]{svmsmote()}},
\code{\link[=tomek]{tomek()}}
}
\concept{Direct Implementations}
