TCLUST is a model-based clustering methodology, which employs trimming and restrictions for getting robust estimators. It is available in the tclust package at the CRAN website and in the FSDA Matlab library. Extensions of TCLUST modelling include clustering around linear subspaces, factor analyzers approaches and fuzzy proposals. Further research has been focused in allowing more flexible models for the components, based on the skew normal distribution. An important issue that may appear within TCLUST is the dependence of the obtained solutions from the input parameters. Therefore, a variety of tools have been developed to assist to the users in choosing these parameters. Theoretical and robustness properties for the TCLUST estimators have been proven, and many empirical evidences show the efficacy of the proposed methodology, in a wide variety of situations

Escudero, L., Greselin, F., Iscar, A. (2017). New proposals for clustering based on trimming and restrictions. In F. Greselin, Mola, F, M. Zenga (a cura di), Cladag 2017 Book of Short Papers (pp. 676-681). Mantova : Universitas Studiorum S.r.l..

New proposals for clustering based on trimming and restrictions

Greselin, F;
2017

Abstract

TCLUST is a model-based clustering methodology, which employs trimming and restrictions for getting robust estimators. It is available in the tclust package at the CRAN website and in the FSDA Matlab library. Extensions of TCLUST modelling include clustering around linear subspaces, factor analyzers approaches and fuzzy proposals. Further research has been focused in allowing more flexible models for the components, based on the skew normal distribution. An important issue that may appear within TCLUST is the dependence of the obtained solutions from the input parameters. Therefore, a variety of tools have been developed to assist to the users in choosing these parameters. Theoretical and robustness properties for the TCLUST estimators have been proven, and many empirical evidences show the efficacy of the proposed methodology, in a wide variety of situations
Capitolo o saggio
model based clustering, robustness, trimming, constraints
English
Cladag 2017 Book of Short Papers
Greselin, F; Mola; F; Zenga, M
13-set-2017
2017
978-88-99459-71-0
Universitas Studiorum S.r.l.
676
681
Escudero, L., Greselin, F., Iscar, A. (2017). New proposals for clustering based on trimming and restrictions. In F. Greselin, Mola, F, M. Zenga (a cura di), Cladag 2017 Book of Short Papers (pp. 676-681). Mantova : Universitas Studiorum S.r.l..
open
File in questo prodotto:
File Dimensione Formato  
GGM Cladag 2017 New proposals fpr clustering.pdf

accesso aperto

Dimensione 417.25 kB
Formato Adobe PDF
417.25 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/171221
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
Social impact