P-splines based clustering as a general framework: Some applications using different clustering algorithms

Clustering; P-spline; Time course data; Unsupervised learning; Cluster analysis; Based clustering; Clustering methods; Dimensional vectors; Hierarchical clustering algorithms; P-splines; Time course datum; Clustering algorithms

Abstract :

[en] A parsimonious clustering method suitable for time course data applications has been recently introduced. The idea behind this proposal is quite simple but efficient. Each series is first summarized by lower dimensional vectors of P-spline coefficients and then, the P-spline coefficients are partitioned by means of a suitable clustering algorithm. In this paper, we investigate the performance of this proposal through several applications showing examples within both hierarchical and non-hierarchical clustering algorithms. © Springer International Publishing AG 2018.

Disciplines :

Social & behavioral sciences, psychology: Multidisciplinary, general & others
Quantitative methods in economics & management

Author, co-author :

Iorio, Carmela; Department of Industrial Engineering, University of Naples Federico II, Napoli, Italy

Frasso, Gianluca ; Université de Liège - ULiège > Faculté des sciences sociales > Méthodes quantitatives en sciences sociales

D’Ambrosio, Antonio; Department of Economics and Statistics, University of Naples Federico II, Napoli, Italy

Siciliano, Roberta; Department of Industrial Engineering, University of Naples Federico II, Napoli, Italy

Mola, F.

Conversano, C.

Vichi, M.

Language :

English

Title :

P-splines based clustering as a general framework: Some applications using different clustering algorithms

Publication date :

2018

Event name :

10th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2015

Event date :

8 October 2015 through 10 October 2015

Audience :

International

Main work title :

Classification, (Big) Data Analysis and Statistical Learning

Editor :

Mola, Francisco

Conversano, Claudio

Vichi, Maurizio

Publisher :

Springer Berlin Heidelberg

Collection name :

Studies in Classification, Data Analysis, and Knowledge Organization; 215879

Pages :

183-190

Peer reviewed :

Peer reviewed

Available on ORBi :

since 13 October 2020

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