Skip to content

IIndex

I-Index (I).

I-Index (I) 1 adopts the maximum distance between cluster centers. It also shares the type of formulation numerator-separation/denominator-compactness. For compactness, the distance from a data point to its cluster center is also used like CH.

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.IIndex()

>>> 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
IIndex: 6.836566

References

Methods

get

Return the current value of the metric.

revert

Revert the metric.

Parameters

  • x
  • y_pred
  • centers
  • sample_weight – defaults to 1.0
  • correction – defaults to None
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)

  1. Maulik, U., Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices. In: IEEE Transactions on Pattern Analysis and Machine Intelligence 24(12) 1650 - 1654. DOI: 10.1109/TPAMI.2002.1114856 ↩