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Journal articles on the topic 'Functional data clustering'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Tarpey, Thaddeus, and Kimberly K. J. Kinateder. "Clustering Functional Data." Journal of Classification 20, no. 1 (2003): 93–114. http://dx.doi.org/10.1007/s00357-003-0007-3.

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2

Miller, Forrest, James Neill, and Haiyan Wang. "Nonparametric clustering of functional data." Statistics and Its Interface 1, no. 1 (2008): 47–62. http://dx.doi.org/10.4310/sii.2008.v1.n1.a5.

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3

ANTONIADIS, ANESTIS, XAVIER BROSSAT, JAIRO CUGLIARI, and JEAN-MICHEL POGGI. "CLUSTERING FUNCTIONAL DATA USING WAVELETS." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 01 (2013): 1350003. http://dx.doi.org/10.1142/s0219691313500033.

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We present two strategies for detecting patterns and clusters in high-dimensional time-dependent functional data. The use on wavelet-based similarity measures, since wavelets are well suited for identifying highly discriminant local time and scale features. The multiresolution aspect of the wavelet transform provides a time-scale decomposition of the signals allowing to visualize and to cluster the functional data into homogeneous groups. For each input function, through its empirical orthogonal wavelet transform the first strategy uses the distribution of energy across scales to generate a re

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4

Floriello, Davide, and Valeria Vitelli. "Sparse clustering of functional data." Journal of Multivariate Analysis 154 (February 2017): 1–18. http://dx.doi.org/10.1016/j.jmva.2016.10.008.

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5

Kim, Joonpyo, and Hee-Seok Oh. "Pseudo-quantile functional data clustering." Journal of Multivariate Analysis 178 (July 2020): 104626. http://dx.doi.org/10.1016/j.jmva.2020.104626.

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6

Jacques, Julien, and Cristian Preda. "Functional data clustering: a survey." Advances in Data Analysis and Classification 8, no. 3 (2013): 231–55. http://dx.doi.org/10.1007/s11634-013-0158-y.

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7

Lim, Yaeji, Hee-Seok Oh, and Ying Kuen Cheung. "Multiscale Clustering for Functional Data." Journal of Classification 36, no. 2 (2019): 368–91. http://dx.doi.org/10.1007/s00357-019-09313-9.

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8

Álvarez-Esteban, Pedro C., and Luis A. García-Escudero. "Robust clustering of functional directional data." Advances in Data Analysis and Classification 16, no. 1 (2021): 181–99. http://dx.doi.org/10.1007/s11634-021-00482-3.

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AbstractA robust approach for clustering functional directional data is proposed. The proposal adapts “impartial trimming” techniques to this particular framework. Impartial trimming uses the dataset itself to tell us which appears to be the most outlying curves. A feasible algorithm is proposed for its practical implementation justified by some theoretical properties. A “warping” approach is also introduced which allows including controlled time warping in that robust clustering procedure to detect typical “templates”. The proposed methodology is illustrated in a real data analysis problem wh

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9

James, Gareth M., and Catherine A. Sugar. "Clustering for Sparsely Sampled Functional Data." Journal of the American Statistical Association 98, no. 462 (2003): 397–408. http://dx.doi.org/10.1198/016214503000189.

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10

Ma, Ping, Wenxuan Zhong, Yang Feng, and Jun S. Liu. "Bayesian Functional Data Clustering for Temporal Microarray Data." International Journal of Plant Genomics 2008 (April 17, 2008): 1–4. http://dx.doi.org/10.1155/2008/231897.

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We propose a Bayesian procedure to cluster temporal gene expression microarray profiles, based on a mixed-effect smoothing-spline model, and design a Gibbs sampler to sample from the desired posterior distribution. Our method can determine the cluster number automatically based on the Bayesian information criterion, and handle missing data easily. When applied to a microarray dataset on the budding yeast, our clustering algorithm provides biologically meaningful gene clusters according to a functional enrichment analysis.

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11

Schmutz, Amandine, Julien Jacques, Charles Bouveyron, Laurence Chèze, and Pauline Martin. "Clustering multivariate functional data in group-specific functional subspaces." Computational Statistics 35, no. 3 (2020): 1101–31. http://dx.doi.org/10.1007/s00180-020-00958-4.

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12

Bensmail, Halima, Buddana Aruna, O. John Semmes, and Abdelali Haoudi. "Functional Clustering Algorithm for High-Dimensional Proteomics Data." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 80–86. http://dx.doi.org/10.1155/jbb.2005.80.

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Clustering proteomics data is a challenging problem for any traditional clustering algorithm. Usually, the number of samples is largely smaller than the number of protein peaks. The use of a clustering algorithm which does not take into consideration the number of features of variables (here the number of peaks) is needed. An innovative hierarchical clustering algorithm may be a good approach. We propose here a new dissimilarity measure for the hierarchical clustering combined with a functional data analysis. We present a specific application of functional data analysis (FDA) to a high-through

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13

Park, Juhyun, and Jeongyoun Ahn. "Clustering multivariate functional data with phase variation." Biometrics 73, no. 1 (2016): 324–33. http://dx.doi.org/10.1111/biom.12546.

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14

Giraldo, R., P. Delicado, and J. Mateu. "Hierarchical clustering of spatially correlated functional data." Statistica Neerlandica 66, no. 4 (2012): 403–21. http://dx.doi.org/10.1111/j.1467-9574.2012.00522.x.

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15

Ben Slimen, Yosra, Sylvain Allio, and Julien Jacques. "Model-based co-clustering for functional data." Neurocomputing 291 (May 2018): 97–108. http://dx.doi.org/10.1016/j.neucom.2018.02.055.

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16

Yassouridis, Christina, and Friedrich Leisch. "Benchmarking different clustering algorithms on functional data." Advances in Data Analysis and Classification 11, no. 3 (2016): 467–92. http://dx.doi.org/10.1007/s11634-016-0261-y.

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17

Jacques, Julien, and Cristian Preda. "Model-based clustering for multivariate functional data." Computational Statistics & Data Analysis 71 (March 2014): 92–106. http://dx.doi.org/10.1016/j.csda.2012.12.004.

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18

Delaigle, A., P. Hall, and N. Bathia. "Componentwise classification and clustering of functional data." Biometrika 99, no. 2 (2012): 299–313. http://dx.doi.org/10.1093/biomet/ass003.

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19

Dou, Xiaoling, Shingo Shirahata, and Hiroki Sugimoto. "Functional clustering of mouse ultrasonic vocalization data." PLOS ONE 13, no. 5 (2018): e0196834. http://dx.doi.org/10.1371/journal.pone.0196834.

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20

Buerki, M., K. O. Lovblad, H. Oswald, et al. "Multiresolution fuzzy clustering of functional MRI data." Neuroradiology 45, no. 10 (2003): 691–99. http://dx.doi.org/10.1007/s00234-003-1026-9.

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21

Hofmans, Joeri, Tim Vantilborgh, and Omar N. Solinger. "k-Centres Functional Clustering." Organizational Research Methods 21, no. 4 (2017): 905–30. http://dx.doi.org/10.1177/1094428117725793.

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In the present paper, we introduce k-centres functional clustering ( k-centres FC), a person-centered method that clusters people with similar patterns of complex, highly nonlinear change over time. We review fundamentals of the methodology and argue how it addresses some of the limitations of the traditional approaches to modeling repeated measures data. The usefulness of k-centres FC is demonstrated by applying the method to weekly measured commitment data from 109 participants who reported psychological contract breach events. The k-centres FC analysis shows two substantively meaningful clu

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22

Nguyen, XuanLong, and Alan E. Gelfand. "The Dirichlet labeling process for clustering functional data." Statistica Sinica 21, no. 3 (2011): 1249–89. http://dx.doi.org/10.5705/ss.2008.285.

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23

Golovkine, Steven, Nicolas Klutchnikoff, and Valentin Patilea. "Clustering multivariate functional data using unsupervised binary trees." Computational Statistics & Data Analysis 168 (April 2022): 107376. http://dx.doi.org/10.1016/j.csda.2021.107376.

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24

Lee, Miae, Johan Lim, Chungun Park, and Kyeong Eun Lee. "Functional clustering for clubfoot data: A case study." Journal of the Korean Data and Information Science Society 25, no. 5 (2014): 1069–77. http://dx.doi.org/10.7465/jkdi.2014.25.5.1069.

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25

Chiou, Jeng-Min, and Pai-Ling Li. "Functional clustering and identifying substructures of longitudinal data." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69, no. 4 (2007): 679–99. http://dx.doi.org/10.1111/j.1467-9868.2007.00605.x.

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26

Salli, E., H. J. Aronen, S. Savolainen, A. Korvenoja, and A. Visa. "Contextual clustering for analysis of functional MRI data." IEEE Transactions on Medical Imaging 20, no. 5 (2001): 403–14. http://dx.doi.org/10.1109/42.925293.

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27

Delaigle, Aurore, Peter Hall, and Tung Pham. "Clustering functional data into groups by using projections." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81, no. 2 (2019): 271–304. http://dx.doi.org/10.1111/rssb.12310.

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28

Haggarty, R. A., C. A. Miller, and E. M. Scott. "Spatially weighted functional clustering of river network data." Journal of the Royal Statistical Society: Series C (Applied Statistics) 64, no. 3 (2014): 491–506. http://dx.doi.org/10.1111/rssc.12082.

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29

Ciollaro, Mattia, Christopher R. Genovese, and Daren Wang. "Nonparametric clustering of functional data using pseudo-densities." Electronic Journal of Statistics 10, no. 2 (2016): 2922–72. http://dx.doi.org/10.1214/16-ejs1198.

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30

Zeng, Pengcheng, Jian Qing Shi, and Won-Seok Kim. "Simultaneous Registration and Clustering for Multidimensional Functional Data." Journal of Computational and Graphical Statistics 28, no. 4 (2019): 943–53. http://dx.doi.org/10.1080/10618600.2019.1607744.

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31

emeriau, s., l. pierot, j. b. poline, and e. bittar. "A new functional characterization for fMRI data clustering." NeuroImage 47 (July 2009): S80. http://dx.doi.org/10.1016/s1053-8119(09)70556-7.

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32

Secchi, Piercesare, Simone Vantini, and Valeria Vitelli. "A clustering algorithm for spatially dependent functional data." Procedia Environmental Sciences 7 (2011): 176–81. http://dx.doi.org/10.1016/j.proenv.2011.07.031.

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33

Romano, Elvira, Antonio Balzanella, and Rosanna Verde. "Spatial variability clustering for spatially dependent functional data." Statistics and Computing 27, no. 3 (2016): 645–58. http://dx.doi.org/10.1007/s11222-016-9645-2.

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34

Slaets, Leen, Gerda Claeskens, and Mia Hubert. "Phase and amplitude-based clustering for functional data." Computational Statistics & Data Analysis 56, no. 7 (2012): 2360–74. http://dx.doi.org/10.1016/j.csda.2012.01.017.

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35

Suarez, Adam Justin, and Subhashis Ghosal. "Bayesian Clustering of Functional Data Using Local Features." Bayesian Analysis 11, no. 1 (2016): 71–98. http://dx.doi.org/10.1214/14-ba925.

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36

Chen, Jen-Hao, Yen-Chang Chang, and Wen-Liang Hung. "A self-organizing clustering algorithm for functional data." Communications in Statistics - Simulation and Computation 49, no. 5 (2018): 1237–63. http://dx.doi.org/10.1080/03610918.2018.1494280.

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37

Secchi, Piercesare, Simone Vantini, and Valeria Vitelli. "Bagging Voronoi classifiers for clustering spatial functional data." International Journal of Applied Earth Observation and Geoinformation 22 (June 2013): 53–64. http://dx.doi.org/10.1016/j.jag.2012.03.006.

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38

Liu, Xueli, and Mark C. K. Yang. "Simultaneous curve registration and clustering for functional data." Computational Statistics & Data Analysis 53, no. 4 (2009): 1361–76. http://dx.doi.org/10.1016/j.csda.2008.11.019.

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39

Zambom, Adriano Zanin, Julian A. A. Collazos, and Ronaldo Dias. "Functional data clustering via hypothesis testing k-means." Computational Statistics 34, no. 2 (2018): 527–49. http://dx.doi.org/10.1007/s00180-018-0808-9.

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40

Haggarty, R. A., C. A. Miller, E. M. Scott, F. Wyllie, and M. Smith. "Functional clustering of water quality data in Scotland." Environmetrics 23, no. 8 (2012): 685–95. http://dx.doi.org/10.1002/env.2185.

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41

Wang, TianTian, KeChao Wang, XiaoHong Su, and Lin Liu. "Data Mining in Programs." International Journal of Data Warehousing and Mining 16, no. 2 (2020): 48–63. http://dx.doi.org/10.4018/ijdwm.2020040104.

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Software exists in various control systems, such as security-critical systems and so on. Existing program clustering methods are limited in identifying functional equivalent programs with different syntactic representations. To solve this problem, firstly, a clustering method based on structured metric vectors was proposed to quickly identify structurally similar programs from a large number of existing programs. Next, a clustering method based on similar execution value sequences was proposed, to accurately identify the functional equivalent programs with code variations. This approach has be

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42

Song, Joon Jin, Ho-Jin Lee, Jeffrey S. Morris, and Sanghoon Kang. "Clustering of time-course gene expression data using functional data analysis." Computational Biology and Chemistry 31, no. 4 (2007): 265–74. http://dx.doi.org/10.1016/j.compbiolchem.2007.05.006.

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43

Baragilly, Mohammed, Hend Gabr, and Brian H. Willis. "Clustering functional data using forward search based on functional spatial ranks with medical applications." Statistical Methods in Medical Research 31, no. 1 (2021): 47–61. http://dx.doi.org/10.1177/09622802211002865.

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Cluster analysis of functional data is finding increasing application in the field of medical research and statistics. Here we introduce a functional version of the forward search methodology for the purpose of functional data clustering. The proposed forward search algorithm is based on the functional spatial ranks and is a data-driven non-parametric method. It does not require any preprocessing functional data steps, nor does it require any dimension reduction before clustering. The Forward Search Based on Functional Spatial Rank (FSFSR) algorithm identifies the number of clusters in the cur

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44

Emeriau, S., F. Blanchard, J. B. Poline, L. Pierot, and E. Bittar. "Unsupervised spatio-functional clustering of fMRI data based on new functional feature." International Journal of Signal and Imaging Systems Engineering 5, no. 2 (2012): 93. http://dx.doi.org/10.1504/ijsise.2012.047781.

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45

Lim, Yaeji, Ying Kuen Cheung, and Hee-Seok Oh. "A generalization of functional clustering for discrete multivariate longitudinal data." Statistical Methods in Medical Research 29, no. 11 (2020): 3205–17. http://dx.doi.org/10.1177/0962280220921912.

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This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent mul

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46

Matsui, Hidetoshi, Toshihiro Misumi, Takaaki Yokomizo, and Sadanori Konishi. "Clustering for Functional Data via Nonlinear Mixed Effects Models." Japanese Journal of Applied Statistics 45, no. 1-2 (2016): 25–45. http://dx.doi.org/10.5023/jappstat.45.25.

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47

Yoon, Sanghoo, and Youngjean Choi. "Functional clustering for electricity demand data: A case study." Journal of the Korean Data and Information Science Society 26, no. 4 (2015): 885–94. http://dx.doi.org/10.7465/jkdi.2015.26.4.885.

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48

Wei, Ziwen, and Lynn Kuo. "Nonparametric Bayesian functional clustering for time-course microarray data." Statistics and Its Interface 7, no. 4 (2014): 543–57. http://dx.doi.org/10.4310/sii.2014.v7.n4.a10.

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49

Harada, I., and T. Adachi. "New clustering approach to chip floorplan using functional data." Electronics Letters 23, no. 17 (1987): 900. http://dx.doi.org/10.1049/el:19870636.

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50

Spellman, Paul, Audrey Gasch, Michael Eisen, Camilla Kao, Patrick Brown, and David Botstein. "Functional clustering of genes using microarray gene expression data." Nature Genetics 23, S3 (1999): 75. http://dx.doi.org/10.1038/14406.

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