cvi.GD53

class cvi.GD53

Bases: CVI

Generalized Dunn’s Index 53 (GD53) Cluster Validity Index.

References

    1. Ibrahim, J. M. Keller, and J. C. Bezdek, “Evaluating Evolving Structure in Streaming Data With Modified Dunn’s Indices,” IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-12, 2019.

    1. Moshtaghi, J. C. Bezdek, S. M. Erfani, C. Leckie, and J. Bailey, “Online Cluster Validity Indices for Streaming Data,” ArXiv e-prints, 2018, arXiv:1801.02937v1 [stat.ML].

    1. Moshtaghi, J. C. Bezdek, S. M. Erfani, C. Leckie, J. Bailey, “Online cluster validity indices for performance monitoring of streaming data clustering,” Int. J. Intell. Syst., pp. 1-23, 2018.

      1. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” J. Cybern., vol. 3, no. 3 , pp. 32-57, 1973.

      1. Bezdek and N. R. Pal, “Some new indexes of cluster validity,” IEEE Trans. Syst., Man, and Cybern., vol. 28, no. 3, pp. 301-315, Jun. 1998.

__init__()

Generalized Dunn’s Index 53 (GD53) initialization routine.

Methods

__init__()

Generalized Dunn's Index 53 (GD53) initialization routine.

get_cvi(data, label)

Update the CVI and return its criterion value.

merge(target_label, source_label)

Merge a source cluster into a target cluster.

remove(sample, label)

Remove a sample from an initialized CVI.

Attributes

info

get_cvi(data: ndarray, label: int | ndarray) → float

Update the CVI and return its criterion value.

Pass a one-dimensional sample and scalar integer label for an incremental update, or a two-dimensional batch and label vector for batch initialization. The object is mutated in both modes. A batch may be followed by incremental updates, but a second batch is not supported.

Parameters:

  • data (np.ndarray) – The sample(s) of features used for clustering.

  • label (Union[int, np.ndarray]) – The label(s) prescribed to the sample(s) by the clustering algorithm.

Returns:

The CVI’s criterion value.

Return type:

float

Raises:

ValueError – If the input dimensionality is invalid, feature dimensionality changes after initialization, batch labels contain fewer than two distinct values, or a second batch update is requested.

merge(target_label: int, source_label: int) → float

Merge a source cluster into a target cluster.

The target external label is retained and the source label is removed.

Parameters:

  • target_label (int) – External label of the cluster that remains after the merge.

  • source_label (int) – External label of the cluster merged into the target.

Returns:

The updated CVI criterion value.

Return type:

float

Raises:

  • NotImplementedError – If this index does not implement cluster merging.

  • ValueError – If the index is uninitialized, either label is unknown, or the two labels are equal.

remove(sample: ndarray, label: int) → float

Remove a sample from an initialized CVI.

The caller is responsible for ensuring that the sample belongs to the supplied cluster label. If the sample is the cluster’s final member, the empty cluster and its label are removed.

Parameters:

  • sample (numpy.ndarray) – One sample vector of features.

  • label (int) – External label of the cluster containing the sample.

Returns:

The updated CVI criterion value.

Return type:

float

Raises:

  • NotImplementedError – If this index does not implement removal.

  • ValueError – If the index is uninitialized, the label is unknown, the sample has the wrong shape, or the sample is inconsistent with the stored sufficient statistics.