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KMeans

Extends:

Clusterer → KMeans

k-means clusterer.

Constructor Summary

Public Constructor
public

constructor(optionsUser: Object)

Constructor.

Member Summary

Public Members
public

centroids: *[]

public
public
public
public

Method Summary

Public Methods
public

cluster(X: *): *

public

Initialize the centroids of each of the clusters based on the user's settings

public

train(X: *)

Inherited Summary

From class Clusterer
public

Assign clusters to samples.

public abstract

Run the clustering algorithm on a dataset and obtain the cluster predictions per class.

Public Constructors

public constructor(optionsUser: Object) source

Constructor. Initialize class members and store user-defined options.

Params:

NameTypeAttributeDescription
optionsUser Object
  • optional

User-defined options for KNN

optionsUser.numClusters number
  • optional
  • default: 8

Number of clusters to assign in total

optionsUser.initialization string
  • optional
  • default: 'random'

Initialization procedure for cluster centers. Either 'random', for randomly selecting (without replacement) a datapoint for each cluster center, or 'kmeans++', for initializing cluster centroids with the kmeans++ procedure

Public Members

public centroids: *[] source

public initialization: * source

public numClusters: * source

public numFeatures: * source

public numSamples: * source

Public Methods

public cluster(X: *): * source

Assign clusters to samples.

Override:

Clusterer#cluster

Params:

NameTypeAttributeDescription
X *

Return:

*

See:

public initializeCentroids(X: Array<Array<number>>) source

Initialize the centroids of each of the clusters based on the user's settings

Params:

NameTypeAttributeDescription
X Array<Array<number>>

Features per data point

public train(X: *) source

Run the clustering algorithm on a dataset and obtain the cluster predictions per class.

Override:

Clusterer#train

Params:

NameTypeAttributeDescription
X *

See: