Genie++ Fast and Robust Hierarchical Clustering with Noise Point Detection
Genie++ outputs meaningful clusters and is fast even on large data sets.
The package documentation, tutorials, and benchmarks are available at https://genieclust.gagolewski.com/.
A faster and more powerful version of Genie - a robust and outlier resistant clustering algorithm (see Gagolewski, Bartoszuk, Cena, 2016), originally published as an R package genie.
The idea behind Genie is very simple. First, make each individual point the only member of its own cluster. Then, keep merging pairs of the closest clusters, one after another. However, to prevent the formation of clusters of highly imbalanced sizes a point group of the smallest size is sometimes matched with its nearest neighbours.
Genie's appealing simplicity goes hand in hand with its usability; it often outperforms other clustering approaches such as K-means, BIRCH, or average, Ward, and complete linkage on benchmark data.
Genie is also very fast - determining the whole cluster hierarchy for datasets of millions of points can be completed within a coffee break. Therefore, it is perfectly suited for solving of extreme clustering tasks (large datasets with any number of clusters to detect) for data (also sparse) that fit into memory. Thanks to the use of nmslib, sparse or string inputs are also supported.
It also allows clustering with respect to mutual reachability distances so that it can act as a noise point detector or a robustified version of HDBSCAN* (see Campello et al., 2015) that is able to detect a predefined number of clusters and hence it doesn't dependent on the DBSCAN's somehow difficult-to-set eps parameter.
Author and Contributors
Author and Maintainer: Marek Gagolewski
Computing of some partition similarity scores (namely, the normalised accuracy and pair sets index) is based on an implementation of the shortest augmenting path algorithm for the rectangular assignment problem contributed by Peter M. Larsen.
Python and R Package Features
Implemented algorithms include:
- Genie++ - a reimplementation of the original Genie algorithm (with a scikit-learn-compatible interface; Gagolewski et al., 2016);
- Genie+HDBSCAN* - our robustified (Geniefied) retake on the HDBSCAN* (Campello et al., 2015) method that detects noise points in data and outputs clusters of predefined sizes;
- (Python only, experimental preview) Genie+Ic (GIc) - Cena's (2018) algorithm to minimise the information theoretic criterion discussed by Mueller et al. (2012).
genieclust.GIc (Python) or
- Inequity measures (the normalised Gini and Bonferroni index);
- unctions to compare partitions (adjusted&unadjusted Rand, adjusted&unadjusted Fowlkes-Mallows (FM), adjusted&normalised&unadjusted mutual information (MI) scores, normalised accuracy and pair sets index (PSI));
- (Python only) Union-find (disjoint sets) data structures (with extensions);
- (Python only) Useful R-like plotting functions.
Examples, Tutorials, and Documentation
The Python language version of genieclust has a familiar scikit-learn-like look-and-feel:
import genieclust X = ... # some data g = genieclust.Genie(n_clusters=2) labels = g.fit_predict(X)
R's interface is compatible with
hclust(), but there is more.
X <- ... # some data h <- gclust(X) plot(h) # plot cluster dendrogram cutree(h, k=2) # or genie(X, k=2)
Check out the tutorials and the package documentation at https://genieclust.gagolewski.com/.
How to Install
To install via
pip (see PyPI):
pip3 install genieclust
The package requires Python 3.5+ together with
cython as well as
To build and install the most recent development version, call:
git clone https://github.com/gagolews/genieclust.git cd genieclust python3 setup.py install --user
To install the most recent release, call:
See the package entry on CRAN.
Note that the core functionality is implemented in form of a header-only C++ library, hence it might be relatively easily adapted for use in other environments.
Any contributions are welcome (e.g., Julia, ...).
Copyright (C) 2018-2020 Marek Gagolewski (https://www.gagolewski.com)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License Version 3, 19 November 2007, published by the Free Software Foundation.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License Version 3 for more details. You should have received a copy of the License along with this program. If not, see (https://www.gnu.org/licenses/).
src/c_scipy_rectangular_lsap.h is adapted from the
scipy project (https://scipy.org/scipylib/), source:
Author: Peter M. Larsen. Distributed under the BSD-3-Clause license.
Gagolewski M., Bartoszuk M., Cena A., Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm, Information Sciences 363, 2016, 8-23. doi:10.1016/j.ins.2016.05.003.
Cena A., Gagolewski M., Genie+OWA: Robustifying Hierarchical Clustering with OWA-based Linkages, Information Sciences 520, 2020, 324-336. doi:10.1016/j.ins.2020.02.025.
Cena A., Adaptive hierarchical clustering algorithms based on data aggregation methods, PhD Thesis, Systems Research Institute, Polish Academy of Sciences, 2018.
Campello R., Moulavi D., Zimek A., Sander J., Hierarchical density estimates for data clustering, visualization, and outlier detection, ACM Transactions on Knowledge Discovery from Data 10(1), 2015, 5:1-5:51. doi:10.1145/2733381.
Crouse D.F., On implementing 2D rectangular assignment algorithms, IEEE Transactions on Aerospace and Electronic Systems 52(4), 2016, 1679-1696. doi:10.1109/TAES.2016.140952.
Mueller A., Nowozin S., Lampert C.H., Information Theoretic Clustering using Minimum Spanning Trees, DAGM-OAGM, 2012.
Curtin R.R., Edel M., Lozhnikov M., Mentekidis Y., Ghaisas S., Zhang S., mlpack 3: A fast, flexible machine learning library, Journal of Open Source Software 3(26), 726, 2018. doi:10.21105/joss.00726.
March W.B., Ram P., Gray A.G., Fast Euclidean Minimum Spanning Tree: Algorithm, Analysis, and Applications, Proc. ACM SIGKDD'10, 2010, 603-611.
Naidan B., Boytsov L., Malkov Y., Novak D., Non-metric space library (NMSLIB) manual, version 2.0, 2019. https://github.com/nmslib/nmslib/blob/master/manual/latex/manual.pdf.
Jarník V., O jistem problemu minimalnim, Prace Moravske Prirodovedecke Spolecnosti 6, 1930, 57-63.
Olson C.F., Parallel algorithms for hierarchical clustering, Parallel Computing 21(8), 1995, 1313-1325. doi:10.1016/0167-8191(95)00017-I.
Prim R., Shortest connection networks and some generalizations, The Bell System Technical Journal 36(6), 1957, 1389-1401.
Hubert L., Arabie P., Comparing Partitions, Journal of Classification 2(1), 1985, 193-218. doi:10.1007/BF01908075.
Rezaei M., Franti P., Set matching measures for external cluster validity, IEEE Transactions on Knowledge and Data Mining 28(8), 2016, 2173-2186. doi:10.1109/TKDE.2016.2551240.
Vinh N.X., Epps J., Bailey J., Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance, Journal of Machine Learning Research 11, 2010, 2837-2854.