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Artículos de revistas sobre el tema "Clustering spectral"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Huang, Dong, Chang-Dong Wang, Jian-Sheng Wu, Jian-Huang Lai, and Chee-Keong Kwoh. "Ultra-Scalable Spectral Clustering and Ensemble Clustering." IEEE Transactions on Knowledge and Data Engineering 32, no. 6 (2020): 1212–26. http://dx.doi.org/10.1109/tkde.2019.2903410.

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12

Fu, Li Li, Yong Li Liu, and Li Jing Hao. "Research on Spectral Clustering." Applied Mechanics and Materials 687-691 (November 2014): 1350–53. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1350.

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Spectral clustering algorithm is a kind of clustering algorithm based on spectral graph theory. As spectral clustering has deep theoretical foundation as well as the advantage in dealing with non-convex distribution, it has received much attention in machine learning and data mining areas. The algorithm is easy to implement, and outperforms traditional clustering algorithms such as K-means algorithm. This paper aims to give some intuitions on spectral clustering. We describe different graph partition criteria, the definition of spectral clustering, and clustering steps, etc. Finally, in order

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13

Pang, Yanwei, Jin Xie, Feiping Nie, and Xuelong Li. "Spectral Clustering by Joint Spectral Embedding and Spectral Rotation." IEEE Transactions on Cybernetics 50, no. 1 (2020): 247–58. http://dx.doi.org/10.1109/tcyb.2018.2868742.

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14

Wada, Yuichiro, Shugo Miyamoto, Takumi Nakagama, Léo Andéol, Wataru Kumagai, and Takafumi Kanamori. "Spectral Embedded Deep Clustering." Entropy 21, no. 8 (2019): 795. http://dx.doi.org/10.3390/e21080795.

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We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space. We use a conditional discrete probability distribution defined by a deep neural network as a statistical model. Our strategy is first to estimate the cluster labels of unlabeled data points selected from a high-density region, and then to conduct semi-supervised learning to train the model by using the estimated cluster labels and the remaining unlabeled data points. Lastly, by u

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15

Chi, Yun, Xiaodan Song, Dengyong Zhou, Koji Hino, and Belle L. Tseng. "On evolutionary spectral clustering." ACM Transactions on Knowledge Discovery from Data 3, no. 4 (2009): 1–30. http://dx.doi.org/10.1145/1631162.1631165.

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16

Chen, Jiansheng, Zhengqin Li, and Bo Huang. "Linear Spectral Clustering Superpixel." IEEE Transactions on Image Processing 26, no. 7 (2017): 3317–30. http://dx.doi.org/10.1109/tip.2017.2651389.

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17

Li, Jianyuan, Yingjie Xia, Zhenyu Shan, and Yuncai Liu. "Scalable Constrained Spectral Clustering." IEEE Transactions on Knowledge and Data Engineering 27, no. 2 (2015): 589–93. http://dx.doi.org/10.1109/tkde.2014.2356471.

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18

Yang, Yang, Fumin Shen, Zi Huang, Heng Tao Shen, and Xuelong Li. "Discrete Nonnegative Spectral Clustering." IEEE Transactions on Knowledge and Data Engineering 29, no. 9 (2017): 1834–45. http://dx.doi.org/10.1109/tkde.2017.2701825.

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19

Huang, Shudong, Hongjun Wang, Dingcheng Li, Yan Yang, and Tianrui Li. "Spectral co-clustering ensemble." Knowledge-Based Systems 84 (August 2015): 46–55. http://dx.doi.org/10.1016/j.knosys.2015.03.027.

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20

Langone, Rocco, Marc Van Barel, and Johan A. K. Suykens. "Efficient evolutionary spectral clustering." Pattern Recognition Letters 84 (December 2016): 78–84. http://dx.doi.org/10.1016/j.patrec.2016.08.012.

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21

Alzate, Carlos, and Johan A. K. Suykens. "Hierarchical kernel spectral clustering." Neural Networks 35 (November 2012): 21–30. http://dx.doi.org/10.1016/j.neunet.2012.06.007.

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22

Ozertem, Umut, Deniz Erdogmus, and Robert Jenssen. "Mean shift spectral clustering." Pattern Recognition 41, no. 6 (2008): 1924–38. http://dx.doi.org/10.1016/j.patcog.2007.09.009.

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23

Binkiewicz, N., J. T. Vogelstein, and K. Rohe. "Covariate-assisted spectral clustering." Biometrika 104, no. 2 (2017): 361–77. http://dx.doi.org/10.1093/biomet/asx008.

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24

von Luxburg, Ulrike, Mikhail Belkin, and Olivier Bousquet. "Consistency of spectral clustering." Annals of Statistics 36, no. 2 (2008): 555–86. http://dx.doi.org/10.1214/009053607000000640.

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25

Langone, Rocco, and Johan A. K. Suykens. "Fast kernel spectral clustering." Neurocomputing 268 (December 2017): 27–33. http://dx.doi.org/10.1016/j.neucom.2016.12.085.

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26

Chen, Guangliang, and Gilad Lerman. "Spectral Curvature Clustering (SCC)." International Journal of Computer Vision 81, no. 3 (2008): 317–30. http://dx.doi.org/10.1007/s11263-008-0178-9.

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27

Bolla, Marianna, and Ahmed Elbanna. "Discrepancy minimizing spectral clustering." Discrete Applied Mathematics 243 (July 2018): 286–89. http://dx.doi.org/10.1016/j.dam.2018.02.016.

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28

Yan, Yuguang, Zhihao Xu, Canlin Yang, Jie Zhang, Ruichu Cai, and Michael Kwok-Po Ng. "An Optimal Transport View for Subspace Clustering and Spectral Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16281–89. http://dx.doi.org/10.1609/aaai.v38i15.29563.

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Clustering is one of the most fundamental problems in machine learning and data mining, and many algorithms have been proposed in the past decades. Among them, subspace clustering and spectral clustering are the most famous approaches. In this paper, we provide an explanation for subspace clustering and spectral clustering from the perspective of optimal transport. Optimal transport studies how to move samples from one distribution to another distribution with minimal transport cost, and has shown a powerful ability to extract geometric information. By considering a self optimal transport mode

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29

Chen, Ji, Kaiping Zhan, Qingzhou Li, et al. "Spectral clustering based on histogram of oriented gradient (HOG) of coal using laser-induced breakdown spectroscopy." Journal of Analytical Atomic Spectrometry 36, no. 6 (2021): 1297–305. http://dx.doi.org/10.1039/d1ja00104c.

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Histogram of oriented gradients (HOG) was introduced in the unsupervised spectral clustering in LIBS. After clustering, the spectra of different matrices were clearly distinguished, and the accuracy of quantitative analysis of coal was improved.

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30

Chen, Guangchun, Juan Hu, Hong Peng, Jun Wang, and Xiangnian Huang. "A Spectral Clustering Algorithm Improved by P Systems." International Journal of Computers Communications & Control 13, no. 5 (2018): 759–71. http://dx.doi.org/10.15837/ijccc.2018.5.3238.

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Using spectral clustering algorithm is diffcult to find the clusters in the cases that dataset has a large difference in density and its clustering effect depends on the selection of initial centers. To overcome the shortcomings, we propose a novel spectral clustering algorithm based on membrane computing framework, called MSC algorithm, whose idea is to use membrane clustering algorithm to realize the clustering component in spectral clustering. A tissue-like P system is used as its computing framework, where each object in cells denotes a set of cluster centers and velocity-location model is

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31

Blanza, Jojo. "Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering." Baghdad Science Journal 18, no. 2(Suppl.) (2021): 1001. http://dx.doi.org/10.21123/bsj.2021.18.2(suppl.).1001.

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This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed t

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32

Li, Qiang. "A Spectrum Clustering Algorithm Based on Weighted Fuzzy Similar Matrix." Advanced Materials Research 482-484 (February 2012): 2109–13. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2109.

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Unlike those traditional clustering algorithms, the spectral clustering algorithm can be applied to non-convex sphere of sample spaces and be converged to global optimal. As a entry point that the similar of spectral clustering, introduce improved weighted fuzzy similar matrix to spectral in this paper which avoids influence from parameters changes of fuzzy similar matrix in traditional spectral clustering on clustering effect and improves the effectiveness of clustering. It is more actual and scientific, which is tested based on UCI data set.

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33

Li, Ziyue, Emma L. D'Ambro, Siegfried Schobesberger, et al. "A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements." Atmospheric Chemistry and Physics 20, no. 4 (2020): 2489–512. http://dx.doi.org/10.5194/acp-20-2489-2020.

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Abstract. One of the challenges of understanding atmospheric organic aerosol (OA) particles stems from its complex composition. Mass spectrometry is commonly used to characterize the compositional variability of OA. Clustering of a mass spectral dataset helps identify components that exhibit similar behavior or have similar properties, facilitating understanding of sources and processes that govern compositional variability. Here, we developed an algorithm for clustering mass spectra, the noise-sorted scanning clustering (NSSC), appropriate for application to thermal desorption measurements of

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34

Priebe, Carey E., Youngser Park, Joshua T. Vogelstein, et al. "On a two-truths phenomenon in spectral graph clustering." Proceedings of the National Academy of Sciences 116, no. 13 (2019): 5995–6000. http://dx.doi.org/10.1073/pnas.1814462116.

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Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering—clustering the vertices of a graph based on their spectral embedding—is commonly approached viaK-means (or, more generally, Gaussian mixture model) clustering composed with either Laplacian spectral embedding (LSE) or adjacency spectral embedding (ASE). Recent theoretical results provide deeper understanding of the problem and solutions and lead us to a “two-truths” LSE vs. ASE spectral graph clustering phenomenon convincingly illustrated here via a diffusion

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35

Sinh, Mai Dinh, Ngo Thanh Long, and Trinh Le Hang. "SPATIAL-SPECTRAL FUZZY K-MEANS CLUSTERING FOR REMOTE SENSING IMAGE SEGMENTATION." Vietnam Journal of Science and Technology 56, no. 2 (2018): 257. http://dx.doi.org/10.15625/2525-2518/56/2/10785.

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Spectral clustering is a clustering method based on algebraic graph theory. The clustering effect by using spectral method depends heavily on the description of similarity between instances of the datasets. Althought, spectral clustering has been significant interest in recent times, but the raw spectral clustering is often based on Euclidean distance, but it is impossible to accurately reflect the complexity of the data. Despite having a well-defined mathematical framework, good performance and simplicity, it suffers from several drawbacks, such as it is unable to determine a reasonable clust

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36

Mandal, Jyotsna Kumar, and Parthajit Roy. "A Novel Spectral Clustering based on Local Distribution." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 2 (2015): 361. http://dx.doi.org/10.11591/ijece.v5i2.pp361-370.

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This paper proposed a novel variation of spectral clustering model based on a novel affinitymetric that considers the distribution of the neighboring points to learn the underlayingstructures in the data set. Proposed affinity metric is calculated using Mahalanobis distancethat exploits the concept of outlier detection for identifying the neighborhoods of the datapoints. RandomWalk Laplacian of the representative graph and its spectra has been consideredfor the clustering purpose and the first k number of eigenvectors have been consideredin the second phase of clustering. The model has been te

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37

Mao, Wei, Guihong Wan, and Haim Schweitzer. "Graph Clustering Methods Derived from Column Subset Selection (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23573–75. http://dx.doi.org/10.1609/aaai.v38i21.30479.

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Spectral clustering is a powerful clustering technique. It leverages the spectral properties of graphs to partition data points into meaningful clusters. The most common criterion for evaluating multi-way spectral clustering is NCut. Column Subset Selection is an important optimization technique in the domain of feature selection and dimension reduction which aims to identify a subset of columns of a given data matrix that can be used to approximate the entire matrix. We show that column subset selection can be used to compute spectral clustering and use this to obtain new graph clustering alg

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38

Mizutani, Tomohiko. "Convex programming based spectral clustering." Machine Learning 110, no. 5 (2021): 933–64. http://dx.doi.org/10.1007/s10994-020-05940-1.

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39

Zhao, Qianli, Linlin Zong, Xianchao Zhang, Xinyue Liu, and Hong Yu. "Incomplete multi-view spectral clustering." Journal of Intelligent & Fuzzy Systems 38, no. 3 (2020): 2991–3001. http://dx.doi.org/10.3233/jifs-190380.

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40

Hong Yu, He Jiang, Xianchao Zhang, and Yuansheng Yang. "K_Neighbors Path Based Spectral Clustering." International Journal of Advancements in Computing Technology 4, no. 1 (2012): 50–58. http://dx.doi.org/10.4156/ijact.vol4.issue1.6.

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41

Wang, Hongtao, Ang Li, Bolin Shen, Yuyan Sun, and Hongmei Wang. "Federated Multi-View Spectral Clustering." IEEE Access 8 (2020): 202249–59. http://dx.doi.org/10.1109/access.2020.3036747.

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42

Mohamed, Samar S., and Magdy MA Salama. "Spectral clustering for TRUS images." BioMedical Engineering OnLine 6, no. 1 (2007): 10. http://dx.doi.org/10.1186/1475-925x-6-10.

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43

Liu, Mingming, Bing Liu, Chen Zhang, and Wei Sun. "Spectral Nonlinearly Embedded Clustering Algorithm." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9264561.

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As is well known, traditional spectral clustering (SC) methods are developed based on themanifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label. But, for some high-dimensional and sparse data, such an assumption might be invalid. Consequently, the clustering performance of SC will be degraded sharply in this case. To solve this problem, in this paper, we propose a general spectral embedded framework, which embeds the true cluster assignment matrix for high-dimensional data into a nonlinear space by a pr

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44

Zhou, Peng, Yi-Dong Shen, Liang Du, Fan Ye, and Xuejun Li. "Incremental multi-view spectral clustering." Knowledge-Based Systems 174 (June 2019): 73–86. http://dx.doi.org/10.1016/j.knosys.2019.02.036.

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45

Jiang, Wenhao, Wei Liu, and Fu-lai Chung. "Knowledge transfer for spectral clustering." Pattern Recognition 81 (September 2018): 484–96. http://dx.doi.org/10.1016/j.patcog.2018.04.018.

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46

Chen, Weifu, and Guocan Feng. "Spectral clustering with discriminant cuts." Knowledge-Based Systems 28 (April 2012): 27–37. http://dx.doi.org/10.1016/j.knosys.2011.11.010.

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47

Cai, Yang, Yuanyuan Jiao, Wenzhang Zhuge, Hong Tao, and Chenping Hou. "Partial multi-view spectral clustering." Neurocomputing 311 (October 2018): 316–24. http://dx.doi.org/10.1016/j.neucom.2018.05.053.

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48

Wen, Guoqiu. "Robust self-tuning spectral clustering." Neurocomputing 391 (May 2020): 243–48. http://dx.doi.org/10.1016/j.neucom.2018.11.105.

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49

Chang, Hong, and Dit-Yan Yeung. "Robust path-based spectral clustering." Pattern Recognition 41, no. 1 (2008): 191–203. http://dx.doi.org/10.1016/j.patcog.2007.04.010.

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50

Xiang, Tao, and Shaogang Gong. "Spectral clustering with eigenvector selection." Pattern Recognition 41, no. 3 (2008): 1012–29. http://dx.doi.org/10.1016/j.patcog.2007.07.023.

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