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Awesome GitHub RepositoriesClustering Preprocessing
Dimensionality reduction used to improve the efficacy of density-based clustering algorithms.
Distinct from Data Preprocessing: Specifically focuses on manifold reduction as a precursor to clustering, not general data cleaning
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Awesome Clustering Preprocessing GitHub Repositories
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- lmcinnes/umap
lmcinnes/umap
8,215Ver en GitHubThis project is a manifold learning and non-linear dimensionality reduction library used to project high-dimensional data into lower-dimensional spaces while preserving topological structure. It functions as a parametric embedding framework and a topological data visualization library for identifying clusters and patterns within complex datasets. The library distinguishes itself through parametric neural mapping, which uses neural networks to learn functional mappings that allow for out-of-sample projections and the reconstruction of original data. It supports supervised and semi-supervised d
Reduces high-dimensional data to a lower-dimensional manifold to improve density-based clustering performance.
Pythondimensionality-reductionmachine-learningtopological-data-analysis
