Changelog

To Do

  • Check for NA/NaN/Inf in input matrices.

  • Bring back support for non-numeric data (needs updates in deadwood).

  • Add support for non-square confusion matrices in normalized_pivoted_accuracy and normalized_clustering_accuracy.

1.3.0 (2026-02-23)

  • The package was heavily refactored; common MST-related functions and classes as well as functions from the tools and plots modules were moved to the new deadwood package, which is now required.

  • [BACKWARD INCOMPATIBILITY] Outlier detection based solely on whether a node is a leaf of a minimum spanning tree w.r.t. some mutual reachability distance turned out to be subpar in more detailed experiments, especially for smaller smoothing factors. Note that in the previous versions of the package, this feature was deemed merely experimental; Hence, detect_noise in genie.default and skip_leaves, preprocess, and postprocess elsewhere are no longer available. Instead, use the more universal deadwood package now.

  • [BACKWARD INCOMPATIBILITY] quitefastmst version >= 0.9.1 is now required; the introduced backward-incompatible changes have been addressed. In particular, the definition of mutual reachability distances has changed. Unlike in Campello et al.’s 2013 paper, now the core distance is the distance to the M-th nearest neighbour, not the (M-1)-th one (not including self).

  • [Python] [BACKWARD INCOMPATIBILITY] internal module was renamed core.

  • [BACKWARD INCOMPATIBILITY] Deprecated functions such as mst_from_nn have been removed.

  • [Python] [BACKWARD INCOMPATIBILITY] compute_full_tree is now always True.

  • [BUGFIX] #92: Passing a non-square confusion matrix to normalized_pivoted_accuracy and normalized_clustering_accuracy yields an error as such objects are yet to be supported.

  • [R] gclust and genie now return the computed MST via the mst object attribute. genie returns an object of the class mstclust. This makes it operable with deadwood.

  • [Python] [BUGFIX] Modifying quitefastmst_params via set_state now invalidates the cached MST.

  • [Python] [NEW FEATURE] plots.plot_scatter has new arguments: asp, markers, and colours. The module globals mrk and col were renamed accordingly. However, as mentioned above, plots was moved to deadwood.

  • [Python] [BACKWARD INCOMPATIBILITY] compute_all_cuts in Genie was renamed coarser. If True, labels_ is still a vector representing the requested n_clusters. The coarser-grained labels are now stored in labels_matrix_ whose i-th row represents an (i+1)-partition.

1.2.0 (2025-07-24)

  • [Python and R] Using the new implementation of Euclidean and mutual reachability minimum spanning trees (pretty fast in low dimensional spaces) from the quitefastmst package.

  • [BACKWARD INCOMPATIBILITY] [Python] Seeking approximate near-neighbours with nmslib is no longer supported directly; unfortunately, the package has not been updated for a while.

  • [BACKWARD INCOMPATIBILITY] mlpack is not used anymore.

  • [Python] MSTClusterMixin: A base class for Genie, GIc, and other MST-based clustering algorithms. [later moved to deadwood]

  • [BACKWARD INCOMPATIBILITY] [Python] Genie and GIc: affinity was renamed metric.

1.1.6 (2024-08-22)

  • [Python] The package now works with numpy 2.0.

1.1.5 (2023-10-18)

  • [BACKWARD INCOMPATIBILITY] Inequality measures are no longer referred to as inequity measures.

  • [BACKWARD INCOMPATIBILITY] Some external cluster validity measures were renamed: adjusted_asymmetric_accuracy → normalized_clustering_accuracy, normalized_accuracy → normalized_pivoted_accuracy.

  • [BACKWARD INCOMPATIBILITY] [Python] compare_partitions2 has been removed, as compare_partitions and other partition similarity scores now support both pairs of label vectors (x, y) and confusion matrices (x=C, y=None).

  • [Python and R] New parameter to pair_sets_index: clipped.

  • In normalizing_permutation and external cluster validity measures, the input matrices can now be of the type double.

  • [BUGFIX] [Python] #80: Fixed adjustment for nmslib_n_neighbors in small samples.

  • [BUGFIX] [Python] #82: cluster_validity submodule not imported.

  • [BUGFIX] Some external cluster validity measures now handle NaNs better and are slightly less prone to round-off errors.

1.1.4 (2023-03-31)

1.1.3 (2023-01-17)

  • [R] mst.default now throws an error if any element in the input matrix is missing/infinite.

  • [Python] The call to mlpack.emst that stopped working with the new version of mlpack has been fixed.

1.1.2 (2022-09-17)

  • [Python and R] adjusted_asymmetric_accuracy now accepts confusion matrices with fewer columns than rows. Such “missing” columns are now treated as if they were filled with 0s.

  • [Python and R] pair_sets_index, and normalized_accuracy return the same results for non-symmetric confusion matrices and transposes thereof.

1.1.1 (2022-09-15)

  • [Python] #75: nmslib is now optional.

  • [BUILD TIME] The use of ssize_t was not portable.

1.1.0 (2022-09-05)

  • [Python and R] New function: adjusted_asymmetric_accuracy.

  • [Python and R] Implementations of the so-called internal cluster validity measures discussed in DOI: 10.1016/j.ins.2021.10.004; see our (GitHub-only) CVI package for R. In particular, the generalised Dunn indices are based on the code originally authored by Maciej Bartoszuk. Thanks.

    Functions added (cluster_validity module): calinski_harabasz_index, dunnowa_index, generalised_dunn_index, negated_ball_hall_index, negated_davies_bouldin_index, negated_wcss_index, silhouette_index, silhouette_w_index, wcnn_index.

    These cluster validity measures are discussed in more detail at https://clustering-benchmarks.gagolewski.com/.

  • [BACKWARD INCOMPATIBILITY] normalized_confusion_matrix now solves the maximal assignment problem instead of applying the somewhat primitive partial pivoting.

  • [Python and R] New function: normalizing_permutation

  • [R] New function: normalized_confusion_matrix.

  • [Python and R] New parameter to pair_sets_index: simplified.

  • [Python] New parameters to plots.plot_scatter: axis, title, xlabel, ylabel, xlim, ylim.

1.0.1 (2022-08-08)

  • A paper on the genieclust package is now available: M. Gagolewski, genieclust: Fast and robust hierarchical clustering, SoftwareX 15, 100722, 2021, DOI: 10.1016/j.softx.2021.100722.

  • [Python] plots.plot_scatter now uses a more accessible default palette (from R 4.0.0).

  • [Python and R] New function: devergottini_index.

1.0.0 (2021-04-22)

  • [R] Using mlpack instead of RcppMLPACK (#72). This package is merely suggested, not dependent upon.

0.9.8 (2021-01-08)

  • [Python] Require Python >= 3.7 (implied by numpy).

  • [Python] Require nmslib.

  • [R] Use RcppMLPACK directly; remove dependency on emstreeR.

  • [R] Use tinytest for unit testing instead of testthat.

0.9.4 (2020-07-31)

  • [BUGFIX] [R] Fixed build errors on Solaris.

0.9.3 (2020-07-25)

  • [BUGFIX] [Python] Added code coverage CI. Fixed some minor inconsistencies. Automated the bdist build chain.

  • [R] Updated DESCRIPTION to meet the CRAN policies.

0.9.2 (2020-07-22)

  • [BUGFIX] [Python] Fix broken build script for OS X with no OpenMP.

0.9.1 (2020-07-18)

  • The package has been completely rewritten. The core functionality is now implemented in C++ (with OpenMP).

  • [R] R version is now available.

  • [EXPERIMENTAL] A preliminary version of clustering with respect to DBSCAN*-like mutual reachability distances is now supported.

  • The parallelised Jarník-Prim algorithm now supports on-the-fly distance computations. Euclidean minimum spanning tree can be determined with mlpack, which is much faster in low-dimensional spaces.

  • [EXPERIMENTAL] [Python] The GIc algorithm proposed by Anna Cena in her 2018 PhD thesis wad added.

  • [Python] Approximate version based on nearest neighbour graphs produced by nmslib was added.

0.1a2 (2018-05-23)

  • [Python] Initial PyPI release.