Module cluster (1.5.0)

Clustering models. This module is styled after Scikit-Learn's cluster module: https://scikit-learn.org/stable/modules/clustering.html.

Classes

KMeans

KMeans(
    n_clusters: int = 8,
    *,
    init: typing.Literal["kmeans++", "random", "custom"] = "kmeans++",
    init_col: typing.Optional[str] = None,
    distance_type: typing.Literal["euclidean", "cosine"] = "euclidean",
    max_iter: int = 20,
    tol: float = 0.01,
    warm_start: bool = False
)

K-Means clustering.

Parameters
Name	Description
`n_clusters`	`int, default 8` The number of clusters to form as well as the number of centroids to generate. Default to 8.
`init`	`"kmeans++", "random" or "custom", default "kmeans++"` The method of initializing the clusters. Default to "kmeans++" kmeas++: Initializes a number of centroids equal to the n_clusters value by using the k-means++ algorithm. Using this approach usually trains a better model than using random cluster initialization. random: Initializes the centroids by randomly selecting a number of data points equal to the n_clusters value from the input data. custom: Initializes the centroids using a provided column of type bool. Uses the rows with a value of True as the initial centroids. You specify the column to use by using the init_col option.
`init_col`	`str or None, default None` The name of the column to use to initialize the centroids. This column must have a type of bool. If this column contains a value of True for a given row, then uses that row as an initial centroid. The number of True rows in this column must be equal to the value you have specified for the n_clusters option. Only works with init method "custom". Default to None.
`distance_type`	`"euclidean" or "cosine", default "euclidean"` The type of metric to use to compute the distance between two points. Default to "euclidean".
`max_iter`	`int, default 20` The maximum number of training iterations, where one iteration represents a single pass of the entire training data. Default to 20.
`tol`	`float, default 0.01` The minimum relative loss improvement that is necessary to continue training. For example, a value of 0.01 specifies that each iteration must reduce the loss by 1% for training to continue. Default to 0.01.
`warm_start`	`bool, default False` Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run. Default to False.