R-universe search: nmslib

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mlampros

nmslibR:Non Metric Space (Approximate) Library

A Non-Metric Space Library ('NMSLIB' <https://github.com/nmslib/nmslib>) wrapper, which according to the authors "is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the 'NMSLIB' <https://github.com/nmslib/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the 'NMSLIB' <https://github.com/nmslib/nmslib> 'Python' Library.

Maintained by Lampros Mouselimis. Last updated 2 years ago.

approximate-nearest-neighbor-search nmslib non-metric python reticulate cpp openmp

23.2 match 12 stars 5.14 score 23 scripts

jlmelville

RcppHNSW:'Rcpp' Bindings for 'hnswlib', a Library for Approximate Nearest Neighbors

'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R interface by relying on the 'Rcpp' package. See <https://github.com/nmslib/hnswlib> for more on 'hnswlib'. 'hnswlib' is released under Version 2.0 of the Apache License.

Maintained by James Melville. Last updated 3 months ago.

approximate-nearest-neighbor-search hnsw k-nearest-neighbors knn nearest-neighbor-search nmslib rcpp cpp

11.5 match 36 stars 10.07 score 63 scripts 77 dependents

gagolews

genieclust:Fast and Robust Hierarchical Clustering with Noise Points Detection

A retake on the Genie algorithm (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>), which is a robust hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). It is now faster and more memory efficient; determining the whole cluster hierarchy for datasets of 10M points in low dimensional Euclidean spaces or 100K points in high-dimensional ones takes only a minute or so. Allows clustering with respect to mutual reachability distances so that it can act as a noise point detector or a robustified version of 'HDBSCAN*' (that is able to detect a predefined number of clusters and hence it does not dependent on the somewhat fragile 'eps' parameter). The package also features an implementation of inequality indices (e.g., Gini and Bonferroni), external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). See also the 'Python' version of 'genieclust' available on 'PyPI', which supports sparse data, more metrics, and even larger datasets.

Maintained by Marek Gagolewski. Last updated 4 days ago.

cluster-analysis clustering clustering-algorithm data-analysis data-mining data-science genie hdbscan hierarchical-clustering hierarchical-clustering-algorithm machine-learning machine-learning-algorithms mlpack nmslib python python3 sparse cpp openmp

11.0 match 61 stars 7.29 score 13 scripts 5 dependents