Bibliografías temáticas / Clustering spectral

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Literatura académica sobre el tema "Clustering spectral"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Clustering spectral"

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Hess, Sibylle, Wouter Duivesteijn, Philipp Honysz, and Katharina Morik. "The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3788–95. http://dx.doi.org/10.1609/aaai.v33i01.33013788.

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When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clustering and DBSCAN. Both paradigms have their pros and cons. While minimum cut clusterings are sensitive to noise, density-based clusterings have trouble handling clusters with varying densities. In this paper, we propose SPECTACL: a method combining the advantages of both approaches, while solving the two mentioned drawbacks. Our method is easy to implement, such as Spectral Cluster
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Li, Hongmin, Xiucai Ye, Akira Imakura, and Tetsuya Sakurai. "LSEC: Large-scale spectral ensemble clustering." Intelligent Data Analysis 27, no. 1 (2023): 59–77. http://dx.doi.org/10.3233/ida-216240.

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A fundamental problem in machine learning is ensemble clustering, that is, combining multiple base clusterings to obtain improved clustering result. However, most of the existing methods are unsuitable for large-scale ensemble clustering tasks owing to efficiency bottlenecks. In this paper, we propose a large-scale spectral ensemble clustering (LSEC) method to balance efficiency and effectiveness. In LSEC, a large-scale spectral clustering-based efficient ensemble generation framework is designed to generate various base clusterings with low computational complexity. Thereafter, all the base c
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Zhuang, Xinwei, and Sean Hanna. "Space Frame Optimisation with Spectral Clustering." International Journal of Machine Learning and Computing 10, no. 4 (2020): 507–12. http://dx.doi.org/10.18178/ijmlc.2020.10.4.965.

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Sun, Gan, Yang Cong, Qianqian Wang, Jun Li, and Yun Fu. "Lifelong Spectral Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5867–74. http://dx.doi.org/10.1609/aaai.v34i04.6045.

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In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we aim to explore the problem of spectral clustering in a lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC). Its goal is to efficiently learn a model for a new spectral clustering task by selectively transferring previously accumulated experience from k
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Ling Ping, Rong Xiangsheng, and Dong Yongquan. "Incremental Spectral Clustering." Journal of Convergence Information Technology 7, no. 15 (2012): 286–93. http://dx.doi.org/10.4156/jcit.vol7.issue15.34.

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Kim, Jaehwan, and Seungjin Choi. "Semidefinite spectral clustering." Pattern Recognition 39, no. 11 (2006): 2025–35. http://dx.doi.org/10.1016/j.patcog.2006.05.021.

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Challa, Aditya, Sravan Danda, B. S. Daya Sagar, and Laurent Najman. "Power Spectral Clustering." Journal of Mathematical Imaging and Vision 62, no. 9 (2020): 1195–213. http://dx.doi.org/10.1007/s10851-020-00980-7.

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Huang, Jin, Feiping Nie, and Heng Huang. "Spectral Rotation versus K-Means in Spectral Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 431–37. http://dx.doi.org/10.1609/aaai.v27i1.8683.

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Spectral clustering has been a popular data clustering algorithm. This category of approaches often resort to other clustering methods, such as K-Means, to get the final cluster. The potential flaw of such common practice is that the obtained relaxed continuous spectral solution could severely deviate from the true discrete solution. In this paper, we propose to impose an additional orthonormal constraint to better approximate the optimal continuous solution to the graph cut objective functions. Such a method, called spectral rotation in literature, optimizes the spectral clustering objective
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Yousefi, Bardia, Clemente Ibarra-Castanedo, Martin Chamberland, Xavier P. V. Maldague, and Georges Beaudoin. "Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery." Remote Sensing 13, no. 11 (2021): 2125. http://dx.doi.org/10.3390/rs13112125.

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Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering
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JIN, Hui-zhen. "Multilevel spectral clustering with ascertainable clustering number." Journal of Computer Applications 28, no. 5 (2008): 1229–31. http://dx.doi.org/10.3724/sp.j.1087.2008.01229.

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Tesis sobre el tema "Clustering spectral"

1

Shortreed, Susan. "Learning in spectral clustering /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8977.

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Larson, Ellis, and Nelly Åkerblom. "Spectral clustering for Meteorology." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297760.

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Climate is a tremendously complex topic, affecting many aspects of human activity and constantly changing. Defining some structures and rules for how it works is thereof of the utmost importance even though it might only cover a small part of the complexity. Cluster analysis is a tool developed in data analysis that is able to categorize data into groups of similar type. In this paper data from the Swedish Meteorological and Hydrological Institute (SMHI) is clustered to find a partitioning. The cluster analysis used is called Spectral clustering which is a family of methods making use of the s
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Gaertler, Marco. "Clustering with spectral methods." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10101213.

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Masum, Mohammad. "Vertex Weighted Spectral Clustering." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3266.

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Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the v
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Larsson, Johan, and Isak Ågren. "Numerical Methods for Spectral Clustering." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275701.

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The Aviation industry is important to the European economy and development, therefore a study of the sensitivity of the European flight network is interesting. If clusters exist within the network, that could indicate possible vulnerabilities or bottlenecks, since that would represent a group of airports poorly connected to other parts of the network. In this paper a cluster analysis using spectral clustering is performed with flight data from 34 different European countries. The report also looks at how to implement the spectral clustering algorithm for large data sets. After performing the s
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Rossi, Alfred Vincent III. "Temporal Clustering of Finite Metric Spaces and Spectral k-Clustering." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500033042082458.

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Darke, Felix, and Blomkvist Linus Below. "Categorization of songs using spectral clustering." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297763.

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A direct consequence of the world becoming more digital is that the amount of available data grows, which presents great opportunities for organizations, researchers and institutions alike.However, this places a huge demand on efficient and understandable algorithms for analyzing vast datasets. This project is centered around using one of these algorithms for identifying groups of songs in a public dataset released by Spotify in 2018. This problem is part of a larger problem class, where one wish to assign data into groups, without the preexisting knowledge of what makes the different groups s
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Marotta, Serena. "Alcuni metodi matriciali per lo Spectral Clustering." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14122/.

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L'obiettivo di questa tesi è analizzare nel dettaglio un insieme di tecniche di analisi dei dati, volte alla selezione e al raggruppamento di elementi omogenei, in modo che si possano facilmente interfacciare tra di loro e fornire un utilizzo più semplice per chi opera nel settore.È introdotta la trattazione dei principali metodi di clustering: linkage, k-medie e in particolare spectral clustering, argomento centrale della mia tesi.
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Alshammari, Mashaan. "Graph Filtering and Automatic Parameter Selection for Efficient Spectral Clustering." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24091.

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Spectral clustering is usually used to detect non-convex clusters. Despite being an effective method to detect this type of clusters, spectral clustering has two deficiencies that made it less attractive for the pattern recognition community. First, the graph Laplacian has to pass through eigen-decomposition to find the embedding space. This has been proved to be a computationally expensive process when the number of points is large. Second, spectral clustering used parameters that highly influence its outcome. Tuning these parameters manually would be a tedious process when examining differen
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Azam, Nadia Farhanaz. "Spectral clustering: An explorative study of proximity measures." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28238.

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In cluster analysis, data are clustered into meaningful groups so that the objects in the same group are very similar, and the objects residing in two different groups are different from one another. One such cluster analysis algorithm is called the spectral clustering algorithm, which originated from the area of graph partitioning. The input, in this case, is a similarity matrix, constructed from the pair-wise similarity between data objects. The algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data. The pair-wise similarity between the object
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Libros sobre el tema "Clustering spectral"

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Bolla, Marianna, ed. Spectral Clustering and Biclustering. John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118650684.

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F, Shandarin Sergei, Weinberg David Hal, and United States. National Aeronautics and Space Administration., eds. A test of the adhesion approximation for gravitational clustering. National Aeronautics and Space Administration, 1995.

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Bolla, Marianna. Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley & Sons, Incorporated, John, 2013.

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Bolla, Marianna. Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley & Sons, Incorporated, John, 2013.

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Bolla, Marianna. Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley & Sons, Incorporated, John, 2013.

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Chennubhotla, Srinivas Chakra. Spectral methods for multi-scale feature extraction and data clustering. 2004.

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Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley, 2013.

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Bolla, Marianna. Spectral Clustering and Biclustering of Networks: Large Graphs and Contingency Tables. Wiley & Sons, Limited, John, 2013.

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Definitions and concepts. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0002.

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A network is specified by its links and nodes. However, it can be described by a much wider range of interesting and important topological features. This chapter introduces how a network can be characterized by its microscopic topological features and macroscopic topological features. Microscopic features introduced are degree and clustering coefficients. Macroscopic topological features introduced are the degree distribution; correlation between degrees of connected nodes; modularity; and, the eigenvalue spectrum (which counts the number of closed paths in the graph).
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Capítulos de libros sobre el tema "Clustering spectral"

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Theodoridis, Sergios, and Konstantinos Koutroumbas. "Spectral Clustering." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_606-2.

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Wierzchoń, Sławomir T., and Mieczysław A. Kłopotek. "Spectral Clustering." In Studies in Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69308-8_5.

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Theodoridis, Sergios, and Konstantinos Koutroumbas. "Spectral Clustering." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_606.

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Martin, Eric, Samuel Kaski, Fei Zheng, et al. "Spectral Clustering." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_771.

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Anselin, Luc. "Spectral Clustering." In An Introduction to Spatial Data Science with GeoDa. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781032713175-8.

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Tripathy, B. K., S. Anveshrithaa, and Shrusti Ghela. "Spectral Clustering." In Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization. CRC Press, 2021. http://dx.doi.org/10.1201/9781003190554-10.

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Theodoridis, Sergios, and Konstantinos Koutroumbas. "Spectral Clustering." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_606.

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Wang, Liang, Christopher Leckie, Kotagiri Ramamohanarao, and James Bezdek. "Approximate Spectral Clustering." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_15.

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Jiang, Wenhao, and Fu-lai Chung. "Transfer Spectral Clustering." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33486-3_50.

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Gong, Yun-Chao, and Chuanliang Chen. "Locality Spectral Clustering." In AI 2008: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3_34.

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Actas de conferencias sobre el tema "Clustering spectral"

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Achten, Sonny, David Winant, and Johan Suykens. "Generative Kernel Spectral Clustering." In ESANN 2025. Ciaco - i6doc.com, 2025. https://doi.org/10.14428/esann/2025.es2025-37.

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Guo, Wengang, and Wei Ye. "Deep Spectral Clustering via Joint Spectral Embedding and Kmeans." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831693.

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Saito, Yuika, Takahiro Kondo, and Kota Uchiyama. "Selective Accumulation of SERS Signal." In JSAP-Optica Joint Symposia. Optica Publishing Group, 2024. https://doi.org/10.1364/jsapo.2024.17a_a34_1.

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We developed a new method for obtaining surface-enhanced Raman scattering (SERS) spectra with extremely high sensitivity and spectral resolution[1]. In this method, thousands of SERS spectra are acquired, followed by a data selection procedure based on density-based spatial clustering of applications with noise (DBSCAN)[2]. Each spectrum is recorded by exposure with a single nanosecond laser pulse to avoid the effect of time averaging. The reconstructed spectrum consists of the data which belong to the clusters. The method was applied to a crystal violet (CV) aqueous solution 10−7 mol/L.
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Chakeri, Alireza, Hamidreza Farhidzadeh, and Lawrence O. Hall. "Spectral sparsification in spectral clustering." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899979.

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Wang, Xiang, and Ian Davidson. "Active Spectral Clustering." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.119.

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Zhao, Bin, and Changshui Zhang. "Compressed Spectral Clustering." In 2009 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2009. http://dx.doi.org/10.1109/icdmw.2009.22.

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Yoo, Shinjae, Hao Huang, and Shiva Prasad Kasiviswanathan. "Streaming spectral clustering." In 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016. http://dx.doi.org/10.1109/icde.2016.7498277.

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Liu, Hongfu, Tongliang Liu, Junjie Wu, Dacheng Tao, and Yun Fu. "Spectral Ensemble Clustering." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. http://dx.doi.org/10.1145/2783258.2783287.

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Blaschko, Matthew B., and Christoph H. Lampert. "Correlational spectral clustering." In 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587353.

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Hunter, Blake, Thomas Strohmer, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Compressive Spectral Clustering." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3498187.

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Informes sobre el tema "Clustering spectral"

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Neville, Jennifer, Micah Adler, and David Jensen. Spectral Clustering with Links and Attributes. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada472209.

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Blakely, Logan. Spectral Clustering for Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6567.

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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of ext
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