P-Splines Based Clustering as a General Framework: Some Applications Using Different Clustering Algorithms

  • Carmela IorioEmail author
  • Gianluca Frasso
  • Antonio D’Ambrosio
  • Roberta Siciliano
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


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.


P-spline Clustering Unsupervised learning Time course data 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Carmela Iorio
    • 1
    Email author
  • Gianluca Frasso
    • 2
  • Antonio D’Ambrosio
    • 3
  • Roberta Siciliano
    • 1
  1. 1.Department of Industrial EngineeringUniversity of Naples Federico IINapoliItaly
  2. 2.Faculté des Sciences SocialesUniversity of LiégeLiégeBelgium
  3. 3.Department of Economics and StatisticsUniversity of Naples Federico IINapoliItaly

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