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Incremental k-means.

The most common way to implement batch k-means is to use Lloyd's algorithm, which consists in assigning all the data points to a set of cluster centers and then moving the centers accordingly. This requires multiple passes over the data and thus isn't applicable in a streaming setting.

In this implementation we start by finding the cluster that is closest to the current observation. We then move the cluster's central position towards the new observation. The halflife parameter determines by how much to move the cluster toward the new observation. You will get better results if you scale your data appropriately.


  • n_clusters – defaults to 5

    Maximum number of clusters to assign.

  • halflife – defaults to 0.5

    Amount by which to move the cluster centers, a reasonable value if between 0 and 1.

  • mu – defaults to 0

    Mean of the normal distribution used to instantiate cluster positions.

  • sigma – defaults to 1

    Standard deviation of the normal distribution used to instantiate cluster positions.

  • p – defaults to 2

    Power parameter for the Minkowski metric. When p=1, this corresponds to the Manhattan distance, while p=2 corresponds to the Euclidean distance.

  • seed (int) – defaults to None

    Random seed used for generating initial centroid positions.


  • centers (dict)

    Central positions of each cluster.


In the following example the cluster assignments are exactly the same as when using sklearn's batch implementation. However changing the halflife parameter will produce different outputs.

>>> from river import cluster
>>> from river import stream

>>> X = [
...     [1, 2],
...     [1, 4],
...     [1, 0],
...     [4, 2],
...     [4, 4],
...     [4, 0]
... ]

>>> k_means = cluster.KMeans(n_clusters=2, halflife=0.4, sigma=3, seed=0)

>>> for i, (x, _) in enumerate(stream.iter_array(X)):
...     k_means = k_means.learn_one(x)
...     print(f'{X[i]} is assigned to cluster {k_means.predict_one(x)}')
[1, 2] is assigned to cluster 1
[1, 4] is assigned to cluster 1
[1, 0] is assigned to cluster 0
[4, 2] is assigned to cluster 0
[4, 4] is assigned to cluster 0
[4, 0] is assigned to cluster 0

>>> k_means.predict_one({0: 0, 1: 0})

>>> k_means.predict_one({0: 4, 1: 4})



Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.


Update the model with a set of features x.


  • x (dict)


Clusterer: self


Equivalent to k_means.learn_one(x).predict_one(x), but faster.


  • x

Predicts the cluster number for a set of features x.


  • x (dict)


int: A cluster number.