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 table of matrices.

  • ModelParameters[1][I][J]: Value of matrix at row I and column J. The rows represent the clusters. The columns represent the features.

  • ModelParameters[2][I][1]: Value of matrix at row I. The rows represent the number of data points associated with each clusters.

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, mode: 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

  • mode: The mode that the model will use to train its model parameters:

    • Hybrid (Default)

    • Batch

    • Sequential

  • 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, mode: 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

  • mode: The mode that the model will use to train its model parameters:

    • Hybrid (Default)

    • Batch

    • Sequential

  • 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, returnOriginalOutput: boolean): Matrix, Matrix -OR- Matrix

Parameters:

  • featureMatrix: Matrix containing data.

  • returnOriginalOutput: Set whether or not to return distance matrix instead of clusterNumberVector and closestDistanceVector.

Returns:

  • clusterNumberVector: A vector containing which cluster that the data belongs to.

  • closestDistanceVector: A vector containing the closest distance between the datapoint and the center of the cluster (centroids).

-OR-

  • distanceMatrix: A matrix containing data-cluster pair distance.

Inherited From

References