genieclust: Fast and Robust Hierarchical Clustering with Noise Point Detection¶
Genie outputs meaningful clusters and is fast even for large data sets.
—by Marek Gagolewski
A faster and more powerful version of Genie - a robust and outlier resistant clustering algorithm, originally published as an R package genie.
The Genie algorithm 1 is based on a minimum spanning tree (MST) of the pairwise distance graph of an input point set. Just like the single linkage, it consumes the edges of the MST in increasing order of weights. However, it prevents the formation of clusters of highly imbalanced sizes; once the Gini index of the cluster size distribution raises above an assumed threshold, a point group of the smallest size is forced to merge with its nearest neighbouring cluster.
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 10M points in low dimensional Euclidean spaces or 100K points in high dimensional ones takes 1-2 minutes. There’s also an approximate version, based on nmslib 3, that is even faster and supports, amongst others, sparse or string inputs. Therefore, it is perfectly suited for solving of extreme clustering tasks (large datasets with any number of clusters to detect) for data that fit into memory.
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* 2 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.
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)
The genieclust package is available for Python (TODO: PyPI–link, how to install) and R (TODO: CRAN–link, how to install). Its source code is distributed under the open source GNU AGPL v3 license and can be downloaded from https://github.com/gagolews/genieclust. 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.
- Comparing Algorithms on Toy Datasets
- Clustering with Noise Points Detection
- Benchmarks (How Good Is It?)
- Timings (How Fast Is It?)
- R Interface Examples
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.
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.
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.