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.
2018
978-3-319-55707-6
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]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/692633
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