Fast k-medoids and q-Fold Fast k-medoids: New distance-based clustering algorithms for large mixed-type data

Aurea Grané Chávez and Fabio Scielzo Ortiz

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: In this work new robust efficient clustering algorithms for large datasets of mixedtype data are proposed and implemented in a new Python package called FastKmedoids. Their performance is analyzed through an extensive simulation study, and compared to a wide range of existing clustering alternatives in terms of both predictive power and computational efficiency. MDS is used to visualize clustering results.

Keywords: Clustering; Fast; k-medoids; Outliers; Robust; mahalanobis; Clustering; Fast; K-Medoids; Generalized; Gower; Multivariate; Heterogeneous; Data; Outliers; Robust; Mahalanobis; Generalized; Gower; Multivariate; heterogeneous; data (search for similar items in EconPapers)
Date: 2025-05-12
New Economics Papers: this item is included in nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://e-archivo.uc3m.es/rest/api/core/bitstreams ... c4df23c887ce/content (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:46673

Access Statistics for this paper

More papers in DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística
Bibliographic data for series maintained by Ana Poveda ().