Skip to content

XieBeni

Xie-Beni index (XB).

The Xie-Beni index 1 has the form of (Compactness)/(Separation), which defines the inter-cluster separation as the minimum squared distance between cluster centers, and the intra-cluster compactness as the mean squared distance between each data object and its cluster centers. The smaller the value of XB, the better the clustering quality.

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.XieBeni()

>>> 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
XieBeni: 0.397043

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. X. L. Xie, G. Beni (1991). A validity measure for fuzzy clustering. In: IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8), 841 - 847. DOI: 10.1109/34.85677 ↩