Robust clustering of functional directional data

Banerjee A, Dhillon I, Ghosh J, Sra S (2003) Generative model-based clustering of directional data. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. pp. 19–28 Cuesta-Albertos JA, Fraiman R (2007) Impartial trimmed \(k\)-means for functional data. Comput Stat Data Anal 51:4864–4877 Cuesta-Albertos JA, Gordaliza A, Matrán C (1997) Trimmed \(k\)-means: an attempt to robustify quantizers. Ann Stat 25:553–576 Ferraty F, Vieu P (2006) Nonparametric functional data analysis. Springer Series in Statistics, Springer, New York García-Escudero L, Gordaliza A, Matrán C, Mayo-Iscar A (2008) A general trimming approach to robust cluster analysis. Ann Stat 36:1324–1345 García-Escudero L, Gordaliza A, Matrán C, Mayo-Iscar A (2010) A review of robust clustering methods. Adv Data Anal Classif 4:89–109 García-Escudero L, Gordaliza A, Matrán C, Mayo-Iscar A, Hennig C (2015) Robustness and Outliers, chapter 29, Chapman & Hall/CRC handbooks of modern statistical methods. Taylor & Francis. pp. 653–678 García-Escudero LA, Gordaliza A (1999) Robustness properties of \(k\) means and trimmed \(k\) means. J Am Stat Assoc 94:956–969 García-Escudero LA, Gordaliza A (2005) A proposal for robust curve clustering. J Classif 22:185–201 García-Escudero LA, Gordaliza A, Matrán C (2003) Trimming tools in exploratory data analysis. J Comput Gr Stat 12:434–449 Giorgino T (2009) Computing and visualizing dynamic time warping alignments in R: the dtw package. J Stat Softw 31(7):1–24 Gordaliza A (1991) Best approximations to random variables based on trimming procedures. J Approx Theor 64:162–180 Hitchcock D, Greenwood M (2015) Robustness and Outliers, chapter 13, Chapman & Hall/CRC Handbooks of modern statistical methods. Taylor & Francis. pp. 265–287 Jacques J, Preda C (2014) Functional data clustering: a survey. Adv Data Anal Classif 8:231–255 Kruskal JB, Liberman M (1983) The symmetric time-warping problem: from continuous to discrete. In: Sankoff D, Kruskal JB (eds) Time Warps, String Edits, and Macromolecules: the Theory and Practice of Sequence Comparison. Addison-Wesley Publishing Company, pp. 125–161 Ley C, Verdebout T (2017) Modern directional statistics. CRC Press Mardia KV, Jupp PE (2009) Directional statistics. Wiley, New York Marron JS, Ramsay JO, Sangalli LM, Srivastava A (2015) Functional data analysis of amplitude and phase variation. Stat Sci 30:468–484 Ramsay JO, Silverman BW (2005) Functional data analysis. Springer Series in Statistics, Springer, New York Ritter G (2015) Robust cluster analysis and variable selection, volume 137 of Monographs on statistics and applied probability. CRC Press, Boca Raton Rivera-García D, García-Escudero LA, Mayo-Iscar A, Ortega J (2019) Robust clustering for functional data based on trimming and constraints. Adv Data Anal Classif 13:201–225 Rousseeuw PJ (1984) Least median of squares regression. J Am stat Assoc 79:871–880 Sakoe H, Chiba S (1971) A dynamic programming approach to continuous speech recognition. In: Proceedings of the seventh international congress on acoustics Sangalli LM, Secchi P, Vantini S, Vitelli V (2010) \(K\)-mean alignment for curve clustering. Comput Stat Data Anal 54(5):1219–1233 Srivastava A, Klassen EP (2016) Functional and shape data analysis. Springer, New York Yassouridis C, Leisch F (2017) Benchmarking different clustering algorithms on functional data. Adv Data Anal Classif 11:467–492