discretize.jointly: Discretize Multivariate Continuous Data by a...
In GridOnClusters: Cluster-Preserving Multivariate Joint Grid Discretization

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/discretize_jointly.R

Discretize multivariate continuous data using a grid that captures the joint distribution via preserving clusters in the original data

1	discretize.jointly(data, k = c(2:10), cluster_label = NULL, min_level = 2)

`data`	a matrix containing two or more continuous variables. Columns are variables, rows are observations.
`k`	either the number or range of clusters to be found on `data`. The default is 2 to 10 clusters. If a range is specified, an optimal k in the range is chosen to maximize the average silhouette width. If `cluster_label` is specified, `k` is ignored.
`cluster_label`	a vector of user-specified cluster labels for each observation in `data`. The user is free to choose any clustering. If unspecified, k-means clustering is used by default.
`min_level`	the minimum number of levels along each dimension

The function implements algorithms described in \insertCiteJwang2020BCBGridOnClusters.

A list that contains four items:

`D`	a matrix that contains the discretized version of the original `data`. Discretized values are one(1)-based.
`grid`	a list of vectors containing decision boundaries for each variable/dimension.
`clabels`	a vector containing cluster labels for each observation in `data`.
`csimilarity`	a similarity score between clusters from joint discretization `D` and cluster labels `clabels`. The score is the adjusted Rand index.

Jiandong Wang, Sajal Kumar and Mingzhou Song

\insertAllCited

See Ckmeans.1d.dp for discretizing univariate continuous data.

# using a specified k
x = rnorm(100)
y = sin(x)
z = cos(x)
data = cbind(x, y, z)
discretized_data = discretize.jointly(data, k=5)$D

# using a range of k
x = rnorm(1000)
y = log1p(abs(x))
z = tan(x)
data = cbind(x, y, z)
discretized_data = discretize.jointly(data, k=c(3:10))$D

# using an alternate clustering method to k-means
library(cluster)
x = rnorm(1000)
y = log1p(abs(x))
z = sin(x)
data = cbind(x, y, z)

# pre-cluster the data using partition around medoids (PAM)
cluster_label = pam(x=data, diss = FALSE, metric = "euclidean", k = 5)$clustering
discretized_data = discretize.jointly(data, cluster_label = cluster_label)$D