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 nonhierarchical clustering algorithms.
P-splines based clustering as a general framework: some applications using different clustering algorithms / Iorio, Carmela; Frasso, Gianluca; D'Ambrosio, Antonio; Siciliano, Roberta. - (2018), pp. 183-190. [10.1007/978-3-319-55708-3_20]
P-splines based clustering as a general framework: some applications using different clustering algorithms.
IORIO, CARMELA
;D'AMBROSIO, ANTONIO;SICILIANO, ROBERTA
2018
Abstract
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 nonhierarchical clustering algorithms.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.