Relevant bibliographies by topics / Multivariate segmentation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Multivariate segmentation'

Author: Grafiati
Published: 22 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Multivariate segmentation"

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Klose, J. "Binary Segmentation for Multivariate Polynomials." Journal of Complexity 11, no. 3 (1995): 330–43. http://dx.doi.org/10.1006/jcom.1995.1015.

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Esteban, Oscar, Gert Wollny, Subrahmanyam Gorthi, et al. "MBIS: Multivariate Bayesian Image Segmentation tool." Computer Methods and Programs in Biomedicine 115, no. 2 (2014): 76–94. http://dx.doi.org/10.1016/j.cmpb.2014.03.003.

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Portillo-García, J., I. Trueba-Santander, G. de Miguel-Vela, and C. Alberola-López. "Efficient multispectral texture segmentation using multivariate statistics." IEE Proceedings - Vision, Image, and Signal Processing 145, no. 5 (1998): 357. http://dx.doi.org/10.1049/ip-vis:19982315.

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Lim, Hyunki, Heeseung Choi, Yeji Choi, and Ig-Jae Kim. "Memetic algorithm for multivariate time-series segmentation." Pattern Recognition Letters 138 (October 2020): 60–67. http://dx.doi.org/10.1016/j.patrec.2020.06.022.

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Hallac, David, Peter Nystrup, and Stephen Boyd. "Greedy Gaussian segmentation of multivariate time series." Advances in Data Analysis and Classification 13, no. 3 (2018): 727–51. http://dx.doi.org/10.1007/s11634-018-0335-0.

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Sudbury, Lynn, and Peter Simcock. "A multivariate segmentation model of senior consumers." Journal of Consumer Marketing 26, no. 4 (2009): 251–62. http://dx.doi.org/10.1108/07363760910965855.

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Omranian, Nooshin, Sebastian Klie, Bernd Mueller-Roeber, and Zoran Nikoloski. "Network-Based Segmentation of Biological Multivariate Time Series." PLoS ONE 8, no. 5 (2013): e62974. http://dx.doi.org/10.1371/journal.pone.0062974.

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Ip, Barry, and Gabriel Jacobs. "Segmentation of the games market using multivariate analysis." Journal of Targeting, Measurement and Analysis for Marketing 13, no. 3 (2005): 275–87. http://dx.doi.org/10.1057/palgrave.jt.5740154.

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Noordam, J. C., W. H. A. M. van den Broek, and L. M. C. Buydens. "Unsupervised segmentation of predefined shapes in multivariate images." Journal of Chemometrics 17, no. 4 (2003): 216–24. http://dx.doi.org/10.1002/cem.794.

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Laksono, Bagaskoro Cahyo, and Ika Yuni Wulansari. "Estimating Customer Lifetime Value in the E-Commerce Industry Using Multivariate Analysis." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (2022): 507–18. http://dx.doi.org/10.34123/icdsos.v2021i1.161.

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Companies can develop their business using big data to support decision-making. Big data in the e-commerce industry that includes size and speed of high transactions can be used to analyze customer behaviour and predict customer value. Nowadays, companies are starting to develop customer-oriented rather than product-oriented business interests. One way that can be used to determine customer value is by calculating Customer Lifetime Value (CLV). By knowing CLV at the individual level, it will be useful to help decision-makers to develop customer segmentation and resource allocation. It is impor
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Dissertations / Theses on the topic "Multivariate segmentation"

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Rzadca, Mark C. "Multivariate granulometry and its application to texture segmentation /." Online version of thesis, 1994. http://hdl.handle.net/1850/12200.

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Templeton, William James. "Consumer interests as market segmentation variables." Thesis, London Business School (University of London), 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312926.

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Rye, Morten Beck. "Image segmentation and multivariate analysis in two-dimensional gel electrophoresis." Doctoral thesis, Norwegian University of Science and Technology, Department of Chemistry, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1744.

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<p>The topic of this thesis is data-analysis on images from two-dimensional electrophoretic gels. Because of the complexity of these images, there are numerous steps and approaches to such an analysis, and no “golden standard” has yet been established on how to produce the desired output. In this thesis focus is put on two essential fields concerning 2D-gel analysis; registration of images by segregation and protein spot identification, and data-analysis on the output of such a registration by multivariate methods. Image segmentation is mainly concerned with the task of identifying individual
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Lu, Jiang. "Transforms for multivariate classification and application in tissue image segmentation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052195.

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Hosseini-Chaleshtari, Jamshid. "Segment Congruence Analysis: An Information Theoretic Approach." PDXScholar, 1987. https://pdxscholar.library.pdx.edu/open_access_etds/797.

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When there are several possible segmentation variables, marketers must investigate the ramifications of their potential interactions. These include their mutual association, the identification of the best (the distinguished) segmentation variable and its predictability by a set of descriptor variables, and the structure of the multivariate system(s) obtained from the segmentation and descriptor variables. This procedure has been defined as segment congruence analysis (SCA). This study utilizes the information theoretic and the log-linear/logit approaches to address a variety of research questi
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Liggett, Rachel Esther. "Multivariate Approaches for Relating Consumer Preference to Sensory Characteristics." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1282868174.

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On, Vu Ngoc Minh. "A new minimum barrier distance for multivariate images with applications to salient object detection, shortest path finding, and segmentation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS454.

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Les représentations hiérarchiques d’images sont largement utilisées dans le traitement d’images pour modéliser le contenu d’une image par un arbre. Une hiérarchie bien connue est l’arbre des formes (AdF) qui encode la relation d’inclusion entre les composants connectés à partir de différents niveaux de seuil. Ce genre d’arbre est auto-duale et invariant de changement de contraste, ce qu’il est utilisé dans de nombreuses applications de vision par ordinateur. En raison de ses propriétés, dans cette thèse, nous utilisons cette représentation pour calculer la nouvelle distance qui appartient au d
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Ghandi, Sanaa. "Analysis of network delay measurements : Data mining methods for completion and segmentation." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2023. http://www.theses.fr/2023IMTA0382.

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La croissance exponentielle d'Internet nécessite une supervision régulière des métriques réseau. Cette thèse se concentre sur les délais aller-retour et la possibilité de résoudre les problèmes de données manquantes et de segmentation multivariée. La première contribution comprend l'orchestration de campagnes de mesure des délais, ainsi que le développement d'un simulateur qui génère des traces de délais de bout en bout. La deuxième contribution de cette thèse est l’introduction de deux méthodes de complétion de données manquantes. La première méthode repose sur la factorisation de matrices no
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Motta, Sergio Luis Stirbolov. "Estudo sobre segmentação de mercado consumidor por atitude e atributos ecológicos de produtos." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-30062009-161308/.

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Este estudo pretendeu verificar se a combinação das variáveis atitude e atributos ecologicamente corretos de bens de consumo pode ser utilizada como base para a segmentação de mercado. Para satisfazer a essa proposição, buscou-se, primeiramente, o domínio da teoria disponível sobre os temas a ela relacionados, que serviu de base à pesquisa de campo. Esta teve um caráter quantitativo e foi do tipo descritivo, com método do estudo de campo; utilizou uma amostra não-probabilística por conveniência de estudantes e professores de uma universidade da cidade de São Paulo-SP, que expressaram suas opin
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Johansson, David. "Automatic Device Segmentation for Conversion Optimization : A Forecasting Approach to Device Clustering Based on Multivariate Time Series Data from the Food and Beverage Industry." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81476.

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This thesis investigates a forecasting approach to clustering device behavior based on multivariate time series data. Identifying an equitable selection to use in conversion optimization testing is a difficult task. As devices are able to collect larger amounts of data about their behavior it becomes increasingly difficult to utilize manual selection of segments in traditional conversion optimization systems. Forecasting the segments can be done automatically to reduce the time spent on testing while increasing the test accuracy and relevance. The thesis evaluates the results of utilizing mult
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Books on the topic "Multivariate segmentation"

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Kleinbaum, Robert M. Multivariate time series forecasts of market share. Marketing Science Institute, 1988.

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Kleinbaum, Robert M. Multivariate time series forecasts of market share. Marketing Science Institute, 1988.

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Book chapters on the topic "Multivariate segmentation"

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Hanselmann, Michael, Ullrich Köthe, Bernhard Y. Renard, Marc Kirchner, Ron M. A. Heeren, and Fred A. Hamprecht. "Multivariate Watershed Segmentation of Compositional Data." In Discrete Geometry for Computer Imagery. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04397-0_16.

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Maya, Shigeru, Akihiro Yamaguchi, Kaneharu Nishino, and Ken Ueno. "Lag-Aware Multivariate Time-Series Segmentation." In Proceedings of the 2020 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.70.

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Ermshaus, Arik, Patrick Schäfer, and Ulf Leser. "Multivariate Human Activity Segmentation: Systematic Benchmark with ClaSP." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-77066-1_2.

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Raj, Jobin, and Govindan V.K. "Unsupervised Color Image Segmentation by Clustering into Multivariate Gaussians." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22786-8_80.

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Küppers, Fabian, Anselm Haselhoff, Jan Kronenberger, and Jonas Schneider. "Confidence Calibration for Object Detection and Segmentation." In Deep Neural Networks and Data for Automated Driving. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_8.

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AbstractCalibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for object detection and segmentation models in this chapter. We introduce the concept of multivariate confidence calibration that is an extension of well-kn
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Lim, Meng-Hui, and Andrew Beng Jin Teoh. "Non-user-Specific Multivariate Biometric Discretization with Medoid-Based Segmentation." In Biometric Recognition. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25449-9_35.

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Gruchalla, Kenny, Mark Rast, Elizabeth Bradley, and Pablo Mininni. "Segmentation and Visualization of Multivariate Features Using Feature-Local Distributions." In Advances in Visual Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24028-7_57.

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Zhuang, Xiahai. "Multivariate Mixture Model for Cardiac Segmentation from Multi-Sequence MRI." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46723-8_67.

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Harańczyk, Grzegorz. "Change Points Detection in Multivariate Signal Applied to Human Activity Segmentation." In Advanced Analytics and Learning on Temporal Data. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49896-1_2.

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Imani, Shima, and Harsh Shrivastava. "tGLAD: A Sparse Graph Recovery Based Approach for Multivariate Time Series Segmentation." In Advanced Analytics and Learning on Temporal Data. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49896-1_12.

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Conference papers on the topic "Multivariate segmentation"

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Fomo, Desmond, and Aki-Hiro Sato. "Enhancing Big Data Analysis: A Recursive Window Segmentation Strategy for Multivariate Longitudinal Data." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825192.

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Velasco-Forero, Santiago, Maider Marin-McGee, and Miguel Velez-Reyes. "Multivariate diffusion tensor and induced segmentation." In 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2013. http://dx.doi.org/10.1109/whispers.2013.8080638.

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Frecon, Jordan, Nelly Pustelnik, Herwig Wendt, and Patrice Abry. "Multivariate optimization for multifractal-based texture segmentation." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351750.

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Leeuwen, Frederique van. "Utilizing Multivariate Time Series for Semantic Segmentation." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006112.

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Zhang, Hui-Juan, and Jia-Cheng Huang. "A segmentation technology for multivariate contextual time series." In 2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2017. http://dx.doi.org/10.1109/iscmi.2017.8279600.

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Lei, Tianhu, and Jayaram K. Udupa. "Multivariate segmentation of fMRI for human brain mapping." In Medical Imaging 2000, edited by Chin-Tu Chen and Anne V. Clough. SPIE, 2000. http://dx.doi.org/10.1117/12.383410.

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Alexandra Constantin, A., B. Ruzena Bajcsy, and C. Sarah Nelson. "Unsupervised segmentation of brain tissue in multivariate MRI." In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2010. http://dx.doi.org/10.1109/isbi.2010.5490406.

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Yang, Zhi, Pengfei Li, Yanxiang Bao, and Xiao Huang. "Speeding Up Multivariate Time Series Segmentation Using Feature Extraction." In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2020. http://dx.doi.org/10.1109/itnec48623.2020.9085218.

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Li, Zhengxin, Jia Liu, and Xiaofeng Zhang. "Similarity Measure of Multivariate Time Series Based on Segmentation." In ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing. ACM, 2020. http://dx.doi.org/10.1145/3383972.3384071.

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Derksen, Harm, Yi Ma, Wei Hong, and John Wright. "Segmentation of multivariate mixed data via lossy coding and compression." In Electronic Imaging 2007, edited by Chang Wen Chen, Dan Schonfeld, and Jiebo Luo. SPIE, 2007. http://dx.doi.org/10.1117/12.714912.

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