Abstract
In this paper, we deal with functional data analysis including functional clustering and an application of functional data analysis. Functional data analysis is proposed by Ramsay et al. In functional data analysis, observed objects are represented by functions. We give an overview of functional data analysis and describe an actual analysis of Music Broadcast Data with functional clustering.
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References
Abraham, C., et al.: Unsupervised curve clustering using B-splines. Scand. J. Statist. 30, 581–595 (2003)
Bosq, D.: Linear processes in functional spaces: theory and applications. Lecture Notes in Statistics, vol. 149. Springer, New York (2000)
Cardot, H., Ferraty, F., Sarda, P.: Spline estimators for the functional linear model. Statistica Sinica 13, 571–591 (2003)
Ferraty, F., Vieu, P.: Nonparametric models for functional data, with application in regression, time-series prediction and curve discrimination. Nonparametric Statistics 16(1-2), 111–125 (2004)
Gower, J.C., Ross, G.J.S.: Minimum spanning trees and single linkage cluster analysis. Appl. Stat. 18, 54–64 (1969)
Hiro, S., et al.: An application of relative projection pursuit for functional data to human growth. In: Proceedings in Computational Statistics, pp. 1113–1120. Physica-Verlag, Heidelberg (2006)
Hoshika, H., et al.: Analysis of Music Broadcast Data with functional clustering (in Japanese). In: Proceedings in Japanese Computational Statistics, pp. 43–46 (2006)
Mizuta, M.: Functional multidimensional scaling. In: Proceedings of the Tenth Japan and Korea Joint Conference of Statistics, pp. 77–82 (2000)
Mizuta, M.: Cluster analysis for functional data. In: Proceedings of the 4th Conference of the Asian Regional Section of the International Association for Statistical Computing, pp. 219–221 (2002)
Mizuta, M.: Hierarchical clustering for functional dissimilarity data. In: Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics, vol. V, pp. 223–227 (2003)
Mizuta, M.: K-means method for functional data. Bulletin of the International Statistical Institute, 54th Session, Book 2, pp. 69–71 (2003)
Mizuta, M.: Clustering methods for functional data. In: Proceedings in Computational Statistics 2004, pp. 1503–1510. Physica-Verlag, Heidelberg (2004)
Mizuta, M.: Multidimensional scaling for dissimilarity functions with several arguments. Bulletin of the International Statistical Institute, 55th Session, p. 244 (2005)
Mizuta, M.: Discrete functional data analysis. In: Proceedings in Computational Statistics 2006, pp. 361–369. Physica-Verlag, Heidelberg (2006)
Nason, G.P.: Functional Projection Pursuit. Computing Science and Statistics 23, 579–582 (1997), http://www.stats.bris.ac.uk/~guy/Research/PP/PP.html
Ramsay, J.O., Silverman, B.W.: Applied Functional Data Analysis – Methods and Case Studies –. Springer, New York (2002)
Ramsay, J.O., Silverman, B.W.: Functional Data Analysis, 2nd edn. Springer, New York (2005)
Shimokawa, M., Mizuta, M., Sato, Y.: An expansion of functional regression analysis (in Japanese). Japanese Journal of Applied Statistics 29(1), 27–39 (2000)
Tarpey, T., Kinateder, K.K.J.: Clustering functional data. J. of Classification 20, 93–114 (2003)
Tokushige, S., Inada, K., Yadohisa, H.: Dissimilarity and related methods for functional data. In: Proceedings of the International Conference on New Trends in Computational Statistics with Biomedical Applications, pp. 295–302 (2001)
Yamanishi, Y., Tanaka, Y.: Geographically weighted functional multiple regression analysis: A numerical investigation. In: Proceedings of the International Conference on New Trends in Computational Statistics with Biomedical Applications, pp. 287–294 (2001)
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Mizuta, M., Kato, J. (2007). Functional Data Analysis and Its Application. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_28
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DOI: https://doi.org/10.1007/978-3-540-72458-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72457-5
Online ISBN: 978-3-540-72458-2
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