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DaviesBouldin

Davies-Bouldin index (DB).

The Davies-Bouldin index (DB) 1 is an old but still widely used inernal validaion measure. DB uses intra-cluster variance and inter-cluster center distance to find the worst partner cluster, i.e., the closest most scattered one for each cluster. Thus, minimizing DB gives us the optimal number of clusters.

Attributes

  • bigger_is_better

    Indicates if a high value is better than a low one or not.

Examples

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

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

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

>>> for x, _ in stream.iter_array(X):
...     k_means = k_means.learn_one(x)
...     y_pred = k_means.predict_one(x)
...     metric = metric.update(x, y_pred, k_means.centers)

>>> metric
DaviesBouldin: 0.22583

Methods

get

Return the current value of the metric.

revert

Revert the metric.

Parameters

  • x
  • y_pred
  • centers
  • sample_weight – defaults to 1.0
update

Update the metric.

Parameters

  • x
  • y_pred
  • centers
  • sample_weight – defaults to 1.0
works_with

Indicates whether or not a metric can work with a given model.

Parameters

  • model (river.base.estimator.Estimator)

References

  1. David L., D., Don, B. (1979). A Cluster Separation Measure. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 1(2), 224 - 227. DOI: 10.1109/TPAMI.1979.4766909 ↩