repliclust: Synthetic Data for Cluster Analysis

Autor:	Zellinger, Michael J., Bühlmann, Peter
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Statistics - Machine Learning Machine Learning (stat.ML) Statistics - Computation Computation (stat.CO) Machine Learning (cs.LG)
Popis:	We present repliclust (from repli-cate and clust-er), a Python package for generating synthetic data sets with clusters. Our approach is based on data set archetypes, high-level geometric descriptions from which the user can create many different data sets, each possessing the desired geometric characteristics. The architecture of our software is modular and object-oriented, decomposing data generation into algorithms for placing cluster centers, sampling cluster shapes, selecting the number of data points for each cluster, and assigning probability distributions to clusters. The project webpage, repliclust.org, provides a concise user guide and thorough documentation. 21 pages, 11 figures
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::901afe26559178f8863beedaaf074679 http://arxiv.org/abs/2303.14301 Zobrazit plný text záznamu