fastcluster: Fast Hierarchical Clustering Routines for R and Python. This is a two-in-one package which provides interfaces to both R and Python. It implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: ”linkage” in the SciPy package ”scipy.cluster.hierarchy”, ”hclust” in R’s ”stats” package, and the ”flashClust” package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide. For information on how to install the Python files, see the file INSTALL in the source distribution.
Keywords for this software
References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- Marek Gagolewski: genieclust: Fast and robust hierarchical clustering (2021) not zbMATH
- Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
- Li, Jia; Seo, Beomseok; Lin, Lin: Optimal transport, mean partition, and uncertainty assessment in cluster analysis (2019)
- Ah-Pine, Julien: An efficient and effective generic agglomerative hierarchical clustering approach (2018)
- Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
- O’Hagan, Adrian; Ferrari, Colm: Model-based and nonparametric approaches to clustering for data compression in actuarial applications (2017)
- Peter Wittek and Shi Gao and Ik Lim and Li Zhao: somoclu: An Efficient Parallel Library for Self-Organizing Maps (2017) not zbMATH
- Botnan, Magnus Bakke; Spreemann, Gard: Approximating persistent homology in Euclidean space through collapses (2015)
- Daniel Müllner: fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python (2013) not zbMATH