API Reference - Models - KMeans

KMeans is an unsupervised machine learning model that predicts which cluster that the input belongs to using distance.

Stored Model Parameters

Contains a matrix.

• ModelParameters[I][J]: Value of matrix at row I and column J. The rows represent the clusters. The columns represent the features.

Constructors

new()

Create new model object. If any of the arguments are nil, default argument values for that argument will be used.

KMeans.new(maximumNumberOfIterations: integer, numberOfClusters: integer, distanceFunction: string, setInitialClustersOnDataPoints: boolean, setTheCentroidsDistanceFarthest: boolean): ModelObject

Parameters:

• maximumNumberOfIterations: How many times should the model needed to be trained.

• numberOfClusters: Number of clusters for model to train and predict on.

• distanceFunction: The function that the model will use to train. distanceFunction available are:

  • Euclidean (Default)

  • Manhattan

  • Cosine

• setInitialClustersOnDataPoints: Set whether or not the model to create centroids on any data points.

• setTheCentroidsDistanceFarthest: Set whether or not the model to create centroids that are furthest from each other. This can only take effect if the “setInitialClustersOnDataPoints” is set to true.

Returns:

• ModelObject: The generated model object.

Functions

setParameters()

Set model’s parameters. When any of the arguments are nil, previous argument values for that argument will be used.

KMeans:setParameters(maximumNumberOfIterations: integer, numberOfClusters: integer, distanceFunction: string, setInitialClustersOnDataPoints: boolean, setTheCentroidsDistanceFarthest: boolean)

Parameters:

• maximumNumberOfIterations: How many times should the model needed to be trained.

• numberOfClusters: Number of clusters for model to train and predict on.

• distanceFunction: The function that the model will use to train. distanceFunction available are:

  • Euclidean

  • Manhattan

  • Cosine

• setInitialClustersOnDataPoints: Set whether or not the model to create centroids on any data points.

• setTheCentroidsDistanceFarthest: Set whether or not the model to create centroids that are furthest from each other. This can only take effect if the “setInitialClustersOnDataPoints” is set to true.

train()

Train the model.

KMeans:train(featureMatrix: Matrix)

Parameters:

• featureMatrix: Matrix containing all data.

Returns:

• costArray: An array containing cost values.

predict()

Predict which clusters does it belong to for a given data.

KMeans:predict(featureMatrix: Matrix): integer, number

Parameters:

• featureMatrix: Matrix containing data.

Returns:

• clusterNumber: The cluster which the data belongs to.

• shortestDistance: The distance between the datapoint and the center of the cluster (centroids).

  Inherited From

• BaseModel