GenerateParcoordForClusters: Generate parallel plot to show each observation and which...

In yhhc2/machinelearnr: Package with various functions for preprocessing, clustering, and classification

Description

Generate parallel plot to show each observation and which cluster they belong in.

Usage

GenerateParcoordForClusters(
  inputted.data,
  cluster.assignment.column,
  x_axis_features
)

Arguments

inputted.data	A dataframe where there are numerical columns for clustering.
cluster.assignment.column	A string that specifies the name of the column in inputted.data that specify which cluster each observation in inputted.data belongs to.
x_axis_features	A vector of strings that specify which features from inputted.data should be displayed on the x-axis of the parallel plot.

Value

No object is returned but a plot will be created.

See Also

Other Clustering functions: CalcOptimalNumClustersForKMeans(), HierarchicalClustering(), generate.2D.clustering.with.labeled.subgroup(), generate.3D.clustering.with.labeled.subgroup(), generate.plots.comparing.clusters()

Examples

example.data <- data.frame(x = c(18, 21, 22, 24, 26, 26, 27, 30, 31,
                                 35, 39, 40, 41, 42, 44, 46, 47, 48, 49, 54, 35, 30),
                          y = c(10, 11, 22, 15, 12, 13, 14, 33, 39, 37, 44,
                                27, 29, 20, 28, 21, 30, 31, 23, 24, 40, 45))

#dev.new()
plot(example.data$x, example.data$y)

km.res <- stats::kmeans(example.data[,c("x", "y")], 3, nstart = 25, iter.max=10)

grouped <- km.res$cluster

example.data <- cbind(example.data, grouped)

GenerateParcoordForClusters(example.data, "grouped", c("x", "y"))

yhhc2/machinelearnr documentation built on Dec. 23, 2021, 7:19 p.m.