Generating multidimensional clusters with support lines

Miniatura indisponível

Data

2023

Título da revista

ISSN da revista

Título do Volume

Editora

Elsevier

Resumo

Synthetic data is essential for assessing clustering techniques, complementing and extending real data, and allowing for more complete coverage of a given problem’s space. In turn, synthetic data generators have the potential of creating vast amounts of data – a crucial activity when real-world data is at premium – while providing a well-understood generation procedure and an interpretable instrument for methodically investigating cluster analysis algorithms. Here, we present Clugen, a modular procedure for synthetic data generation, capable of creating multidimensional clusters supported by line segments using arbitrary distributions. Clugen is open source, comprehensively unit tested and documented, and is available for the Python, R, Julia, and MATLAB/Octave ecosystems. We demonstrate that our proposal can produce rich and varied results in various dimensions, is fit for use in the assessment of clustering algorithms, and has the potential to be a widely used framework in diverse clustering-related research tasks. Keywords: Synthetic data, Clustering, Data generation, Multidimensional data

Descrição

Knowledge-Based Systems

Palavras-chave

SYNTHETIC DATA GENERATION, CRIAÇÃO DE DADOS SINTÉTICOS, CLUSTER ANALYSIS, ANÁLISE DE CLUSTERS, DATA GENERATION, CRIAÇÃO DE DADOS, INFORMÁTICA, COMPUTER SCIENCE

Citação

Fachada, N. & de Andrade, D. (2023). Generating multidimensional clusters with support lines, 277, 110836. https://doi.org/10.1016/j.knosys.2023.110836