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FunCC: A new bi-clustering algorithm for functional data with misalignment

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  • Galvani, Marta
  • Torti, Agostino
  • Menafoglio, Alessandra
  • Vantini, Simone

Abstract

The problem of bi-clustering functional data, which has recently been addressed in literature, is considered. A definition of ideal functional bi-cluster is given and a novel bi-clustering method, called Functional Cheng and Church (FunCC), is developed. The introduced algorithm searches for non-overlapping and non-exhaustive bi-clusters in a set of functions which are naturally ordered in matrix structure through a non-parametric deterministic iterative procedure. Moreover, the possible misalignment of the data, which is a common problem when dealing with functions, is taken into account. Hence, the FunCC algorithm is extended obtaining a model able to jointly bi-cluster and align curves. Different simulation studies are performed to show the potential of the introduced method and to compare it with state-of-the-art methods. The model is also applied on a real case study allowing to discover the spatio-temporal patterns of a bike-sharing system.

Suggested Citation

  • Galvani, Marta & Torti, Agostino & Menafoglio, Alessandra & Vantini, Simone, 2021. "FunCC: A new bi-clustering algorithm for functional data with misalignment," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:csdana:v:160:y:2021:i:c:s0167947321000530
    DOI: 10.1016/j.csda.2021.107219
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    References listed on IDEAS

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    1. Sangalli, Laura M. & Secchi, Piercesare & Vantini, Simone & Veneziani, Alessandro, 2009. "A Case Study in Exploratory Functional Data Analysis: Geometrical Features of the Internal Carotid Artery," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 37-48.
    2. Simone Vantini, 2012. "On the definition of phase and amplitude variability in functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 676-696, December.
    3. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
    4. Kaziska, David & Srivastava, Anuj, 2007. "Gait-Based Human Recognition by Classification of Cyclostationary Processes on Nonlinear Shape Manifolds," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1114-1124, December.
    5. Sangalli, Laura M. & Secchi, Piercesare & Vantini, Simone & Vitelli, Valeria, 2010. "k-mean alignment for curve clustering," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1219-1233, May.
    6. Charles Bouveyron & Laurent Bozzi & Julien Jacques & François‐Xavier Jollois, 2018. "The functional latent block model for the co‐clustering of electricity consumption curves," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(4), pages 897-915, August.
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