DataPredict

API Reference - Models - AgglomerativeHierarchical

AgglomerativeHierarchical clustering groups similar data points into clusters based on distance, in a bottom-up approach.

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.

AgglomerativeHierarchical.new(numberOfClusters: integer, distanceFunction: string, linkageFunction: string): ModelObject

Parameters:

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

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

  • Euclidean (Default)

  • Manhattan

  • Cosine

• linkageFunction: The function to determine how clusters are merged together. Available options are:

  • Minimum (Default)

  • Maximum

  • GroupAverage

  • Ward

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.

AgglomerativeHierarchical:setParameters(numberOfClusters: integer, distanceFunction: string, linkageFunction: string)

Parameters:

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

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

  • Euclidean

  • Manhattan

  • Cosine

• linkageFunction: The function to determine how clusters are merged together. Available options are:

  • Minimum

  • Maximum

  • GroupAverage

  • Ward

train()

Train the model.

AgglomerativeHierarchical:train(featureMatrix: Matrix)

Parameters:

• featureMatrix: Matrix containing all data.

Returns:

• costArray: An array containing cost values.

predict()

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

AgglomerativeHierarchical: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