Relevant bibliographies by topics / Functional data clustering

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Functional data clustering'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Functional data clustering"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Functional data clustering"

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Baragilly, Mohammed Hussein Hassan. "Clustering multivariate and functional data using spatial rank functions." Thesis, University of Birmingham, 2016. http://etheses.bham.ac.uk//id/eprint/7124/.

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In this work, we consider the problem of determining the number of clusters in the multivariate and functional data, where the data are represented by a mixture model in which each component corresponds to a different cluster without any prior knowledge of the number of clusters. For the multivariate case, we propose a new forward search methodology based on spatial ranks. We also propose a modified algorithm based on the volume of central rank regions. Our numerical examples show that it produces the best results under elliptic symmetry and it outperforms the traditional forward search based
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Karmakar, Saurav. "Statistical Stability and Biological Validity of Clustering Algorithms for Analyzing Microarray Data." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/math_theses/3.

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Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we emp
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Vogetseder, Georg. "Functional Analysis of Real World Truck Fuel Consumption Data." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1148.

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<p>This thesis covers the analysis of sparse and irregular fuel consumption data of long</p><p>distance haulage articulate trucks. It is shown that this kind of data is hard to analyse with multivariate as well as with functional methods. To be able to analyse the data, Principal Components Analysis through Conditional Expectation (PACE) is used, which enables the use of observations from many trucks to compensate for the sparsity of observations in order to get continuous results. The principal component scores generated by PACE, can then be used to get rough estimates of the trajectories for
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Arnqvist, Per. "Functional clustering methods and marital fertility modelling." Doctoral thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130734.

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This thesis consists of two parts.The first part considers further development of a model used for marital fertility, the Coale-Trussell's fertility model, which is based on age-specific fertility rates. A new model is suggested using individual fertility data and a waiting time after pregnancies. The model is named the waiting model and can be understood as an alternating renewal process with age-specific intensities. Due to the complicated form of the waiting model and the way data is presented, as given in the United Nation Demographic Year Book 1965, a normal approximation is suggested tog
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Jin, Zhongnan. "Statistical Methods for Multivariate Functional Data Clustering, Recurrent Event Prediction, and Accelerated Degradation Data Analysis." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/102628.

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In this dissertation, we introduce three projects in machine learning and reliability applications after the general introductions in Chapter 1. The first project concentrates on the multivariate sensory data, the second project is related to the bivariate recurrent process, and the third project introduces thermal index (TI) estimation in accelerated destructive degradation test (ADDT) data, in which an R package is developed. All three projects are related to and can be used to solve certain reliability problems. Specifically, in Chapter 2, we introduce a clustering method for multivariate f
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Jiang, Huijing. "Statistical computation and inference for functional data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37087.

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My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data. The first part of my thesis focuses on developing modeling and inference procedure for functional data under spatial dependence. The methodology introduced in this part is motivated by a research study on inequities in accessibility to financial services. The first research problem in this part is concerned with a novel model-based method for clustering random time functions which are spatially interdependent. A cluster consists of time
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Koomson, Obed. "Performance Assessment of The Extended Gower Coefficient on Mixed Data with Varying Types of Functional Data." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3512.

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Clustering is a widely used technique in data mining applications to source, manage, analyze and extract vital information from large amounts of data. Most clustering procedures are limited in their performance when it comes to data with mixed attributes. In recent times, mixed data have evolved to include directional and functional data. In this study, we will give an introduction to clustering with an eye towards the application of the extended Gower coefficient by Hendrickson (2014). We will conduct a simulation study to assess the performance of this coefficient on mixed data whose functio
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Fountalis, Ilias. "From spatio-temporal data to a weighted and lagged network between functional domains: Applications in climate and neuroscience." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55008.

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Spatio-temporal data have become increasingly prevalent and important for both science and enterprises. Such data are typically embedded in a grid with a resolution larger than the true dimensionality of the underlying system. One major task is to identify the distinct semi-autonomous functional components of the spatio-temporal system and to infer their interconnections. In this thesis, we propose two methods that identify the functional components of a spatio-temporal system. Next, an edge inference process identifies the possibly lagged and weighted connections between the system’s compone
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Li, Han. "Statistical Modeling and Analysis of Bivariate Spatial-Temporal Data with the Application to Stream Temperature Study." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/70862.

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Water temperature is a critical factor for the quality and biological condition of streams. Among various factors affecting stream water temperature, air temperature is one of the most important factors related to water temperature. To appropriately quantify the relationship between water and air temperatures over a large geographic region, it is important to accommodate the spatial and temporal information of the steam temperature. In this dissertation, I devote effort to several statistical modeling techniques for analyzing bivariate spatial-temporal data in a stream temperature study. I
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Jonsson, Per. "Improving Clustering of Gene Expression Patterns." Thesis, University of Skövde, Department of Computer Science, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-482.

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<p>The central question investigated in this project was whether clustering of gene expression patterns could be done more biologically accurate by providing the clustering technique with additional information about the genes as input besides the expression levels. With the term biologically accurate we mean that the genes should not only be clustered together according to their similarities in expression profiles, but also according to their functional similarity in terms of functional annotation and metabolic pathway. The data was collected at AstraZeneca R&D Mölndal Sweden and the applied
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Books on the topic "Functional data clustering"

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James, Gareth. Sparseness and functional data analysis. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.11.

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This article considers two functional data analysis settings where sparsity becomes important: the first involves only measurements at a relatively sparse set of points and the second relates to variable selection in a functional case. It begins with a discussion of two data sets that fall into the ‘sparsely observed’ category, the ‘growth’ data and the ‘nephropathy’ data, both of which are used to illustrate alternative approaches for analysing sparse functional data. It then examines different classes of methods that can be applied to functional data, such as basis functions, mixed-effects m
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Arbustini, Eloisa, Valentina Favalli, Alessandro Di Toro, Alessandra Serio, and Jagat Narula. Classification of cardiomyopathies. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198784906.003.0348.

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For over 50 years, the definition and classification of cardiomyopathies have remained anchored in the concept of ventricular dysfunction and myocardial structural remodelling due to unknown cause. The concept of idiopathic was first challenged in 2006, when the American Heart Association classification subordinated the phenotype to the aetiology. Cardiomyopathies were classified as genetic, acquired, and mixed. In 2008, the European Society of Cardiology proposed a phenotype-driven classification that separated familial (genetic) from non-familial (non-genetic) forms of cardiomyopathy. Both c
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Book chapters on the topic "Functional data clustering"

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Romano, Elvira, and Rosanna Verde. "Clustering Geostatistical Functional Data." In Advanced Statistical Methods for the Analysis of Large Data-Sets. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21037-2_3.

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Chiou, Jeng-Min, and Pai-Ling Li. "Functional Clustering of Longitudinal Data." In Contributions to Statistics. Physica-Verlag HD, 2008. http://dx.doi.org/10.1007/978-3-7908-2062-1_17.

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Romano, Elvira, Ramon Giraldo, and Jorge Mateu. "Clustering Spatially Correlated Functional Data." In Contributions to Statistics. Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2736-1_43.

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Secchi, Piercesare, Simone Vantini, and Valeria Vitelli. "Spatial Clustering of Functional Data." In Contributions to Statistics. Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2736-1_44.

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Di Battista, Tonio, Angela De Sanctis, and Francesca Fortuna. "Clustering Functional Data on Convex Function Spaces." In Topics on Methodological and Applied Statistical Inference. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44093-4_11.

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Ingrassia, Salvatore, Andrea Cerioli, and Aldo Corbellini. "Some Issues on Clustering of Functional Data." In Between Data Science and Applied Data Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-18991-3_6.

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Vangel, Mark G. "Combining Functional MRI Data on Multiple Subjects." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_44.

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Conan-Guez, Brieuc, and Fabrice Rossi. "Phoneme Discrimination with Functional Multi-Layer Perceptrons." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_16.

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Boullé, Marc, Romain Guigourès, and Fabrice Rossi. "Nonparametric Hierarchical Clustering of Functional Data." In Advances in Knowledge Discovery and Management. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02999-3_2.

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Preda, Cristian, and Gilbert Saporta. "PLS Approach for Clusterwise Linear Regression on Functional Data." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_17.

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Conference papers on the topic "Functional data clustering"

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Bani, Gabriele, Udo Seiffert, Michael Biehl, and Friedrich Melchert. "Adaptive basis functions for prototype-based classification of functional data." In 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). IEEE, 2017. http://dx.doi.org/10.1109/wsom.2017.8020020.

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Krishna, Ritesh, Chang-Tsun Li, and Vicky Buchanan-Wollaston. "Interaction Based Functional Clustering of Genomic Data." In 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2009. http://dx.doi.org/10.1109/bibe.2009.28.

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Kwon, Amy, and Ming Ouyang. "Clustering of Functional Data by Band Depth." In 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ACM, 2016. http://dx.doi.org/10.4108/eai.3-12-2015.2262364.

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Venkataraman, P. "Data Clustering Using the Natural Bézier Functions." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85103.

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An unorthodox and effective non-iterative procedure for spherical clusters is demonstrated in this paper. It uses natural Bézier functions to determine initial cluster locations using the content of the data. The natural Bernstein-Bézier functions are very robust in representing data through continuous functions in the application of functional data analysis. This paper demonstrates that they are equally robust at resolving data clusters in classification problems. The original data is scaled and segmented. A natural Bézier function is fitted for each segment and the initial clusters are cente
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Hamdan, Muhammad Fauzee, Jamaludin Suhaila, and Abdul Aziz Jemain. "Clustering rainfall pattern in Malaysia using functional data analysis." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907466.

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Ashikh, Vajarala, Gopikrishna Deshpande, D. Rangaprakash, and D. Narayana Dutt. "Clustering of dynamic functional connectivity features obtained from functional Magnetic Resonance Imaging data." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275626.

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Zerrouki, Leila, and Serge Manchon. "A data clustering approach to identify Logical Functional ATFCM Areas." In 2007 IEEE/AIAA 26th Digital Avionics Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/dasc.2007.4391889.

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Bathula, D. R., X. Papademetris, and J. S. Duncan. "LEVEL SET BASED CLUSTERING FOR ANALYSIS OF FUNCTIONAL MRI DATA." In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2007. http://dx.doi.org/10.1109/isbi.2007.356877.

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Emeriau, S., F. Blanchard, JB Poline, L. Pierot, and E. Bittar. "Connectivity feature extraction for spatio-functional clustering of fMRI data." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586776.

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Billings, Jacob C., Alessio Medda, Gordon Berman, and Shella D. Keilholz. "Functional connectivity metrics for wavelet clustering of rs-fMRI data." In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869583.

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Reports on the topic "Functional data clustering"

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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel se
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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