kmeansFit¶
Purpose¶
Partitions data into k clusters, using the kmeans algorithm.
Format¶
-
mdl =
kmeansFit(X, clusters[, ctl])¶ - Parameters
X (NxP matrix) – The training data.
clusters (Scalar) – The number of clusters, or a matrix containing the initial centroids.
ctl –
Optional input, an instance of a
kmeansControlstructure.ctl.initMethod
Scalar specifying the algorithm used to create the initial centroids. Options include:
0
kmeans++ (default).
1
parallel k-means++
2
\(k\) randomly-selected observations.
ctl.nStarts
Scalar, the number of times to run the kmeans algorithm with new starting centroids. Note: this input will be ignored if the clusters input is a starting centroid.
ctl.seed
Seed for the random number generator which creates the initial centroids. Note: this input will be ignored if the clusters input is a starting centroid.
ctl.tolerance
Scalar, the convergence tolerance for the kmeans algorithm.
ctl.maxIters
Scalar, the maximum number of iterations to allow each of the nStarts to run before forcing convergence.
- Returns
mdl (struct) –
An instance of a
kmeansModelstructure.mdl.centroids
kxP matrix, containing the centroids with the lowest intra-cluster sum of squares.
mdl.assignments
Nx1 matrix, containing the centroid assignment for the corresponding observation of the input matrix.
mdl.clusterSS
Scalar, sum of squared differences between each observation and its assigned centroid.
mdl.elapsedIters
Scalar, the number of iterations taken by the start with the lowest clusterSS.
Examples¶
new;
library gml;
// Get dataset with full name
fname = getGAUSSHome() $+ "pkgs/gml/examples/iris.csv";
// Load data
X = loadd(fname, ". -species");
// For repeatable sample
rndseed 234234;
// Split data into x_train and x_test
{ x_train, x_test } = splitData(X, 0.70);
// Number of clusters
n_clusters = 3;
// Declare kmeansModel struct
struct kmeansModel mdl;
// Fit kmeans model
mdl = kmeansFit(x_train , n_clusters);
// Print the k centroids
print mdl.centroids;
The above code will print the following:
5.824 2.710 4.350 1.421
5.009 3.406 1.475 0.250
6.887 3.061 5.732 2.061
References¶
Parallel Kmeans++ initialization. B. Bahmani, B. Moseley, A. Vattani, R. Kumar, S. Vassilvitskii. Scalable K-means++. Proceedings of the VLDB Endowment, 2012.
See also
kmeansFit(), kmeansControlCreate()