Relevant bibliographies by topics / Cluster weights

Contents

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

Academic literature on the topic 'Cluster weights'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Cluster weights"

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Liu, Jing, Fuyuan Cao, Xiao-Zhi Gao, Liqin Yu, and Jiye Liang. "A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4860–67. http://dx.doi.org/10.1609/aaai.v34i04.5922.

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Clustering by jointly exploiting information from multiple views can yield better performance than clustering on one single view. Some existing multi-view clustering methods aim at learning a weight for each view to determine its contribution to the final solution. However, the view-weighted scheme can only indicate the overall importance of a view, which fails to recognize the importance of each inner cluster of a view. A view with higher weight cannot guarantee all clusters in this view have higher importance than them in other views. In this paper, we propose a cluster-weighted kernel k-mea
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Tarter, Michael E., and Stefano Poni. "A Vitis vinifera Cluster's Wing-related Structural Characteristics and Their Associations with Yield and Berry Composition." HortScience 45, no. 8 (2010): 1270–77. http://dx.doi.org/10.21273/hortsci.45.8.1270.

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The hypotheses considered in this article concern the basic question, besides bearing a wing, in what ways do wing-bearing and non-wing-bearing clusters differ? Vines sampled at midseason were again selected at harvest. Each weight of a Vitis vinifera cluster sampled at midseason was multiplied by the number of clusters on the vine from which the cluster had been selected. Correlation coefficients between this quantity and the sampled vine's yield at harvest differed significantly in the sense that coefficients determined solely from the subset of sampled clusters on which a wing (a lateral ar
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HILLIER, DÁNIEL, SERKAN GÜNEL, JOHAN A. K. SUYKENS, and JOOS VANDEWALLE. "PARTIAL SYNCHRONIZATION IN OSCILLATOR ARRAYS WITH ASYMMETRIC COUPLING." International Journal of Bifurcation and Chaos 17, no. 11 (2007): 4177–85. http://dx.doi.org/10.1142/s0218127407019718.

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A new form of cluster synchronization is explored in cellular arrays of chaotic oscillators. Previously, it was shown in the literature that symmetries of the coupling topology with uniform interaction weights lead to several coexisting clusters of synchronized cells. In this study a new phenomenon is presented where highly asymmetric interaction weights can give rise to cluster synchronization regimes with partial synchronization. In addition, cluster or partial synchronization regimes corresponding to asymmetric interaction patterns can break the underlying symmetries of the network topology
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Cristaudo, Vanina, Claude Poleunis, Bartlomiej Czerwinski, and Arnaud Delcorte. "Ar cluster sputtering of polymers: effects of cluster size and molecular weights." Surface and Interface Analysis 46, S1 (2014): 79–82. http://dx.doi.org/10.1002/sia.5424.

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Hummell, Ann K., and David C. Ferree. "Influence of Crop Load and Cluster Microclimate on Yield and Fruit Quality in `Seyval Blanc'." HortScience 31, no. 4 (1996): 575a—575. http://dx.doi.org/10.21273/hortsci.31.4.575a.

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A 2-year field study was initiated in 1994 to examine the interactions between crop load and cluster exposure and their influences on the yield and fruit quality of mature, own-rooted `Seyval blanc' grapevines. Light, moderate, and heavy crop loads were established near bloom by cluster-thinning vines planted at 2.6 × 3.0-m spacing to around 20, 40, and 80 clusters per vine, respectively. At veraison, three clusters per vine were given one of three natural shaded treatments: fully exposed, partially shaded, and densely shaded. Vines with the heavy crop load produced higher yields per vine and
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Adamyan, Larisa, Kirill Efimov, Cathy Y. Chen, and Wolfgang K. Härdle. "Adaptive weights clustering of research papers." Digital Finance 2, no. 3-4 (2020): 169–87. http://dx.doi.org/10.1007/s42521-020-00017-z.

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AbstractThe JEL classification system is a standard way of assigning key topics to economic articles to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of the Collaborative Research Center from Humboldt-Universität zu Berlin we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on http://www.quantlet.de/ and is based on the idea of l
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Karimi, Abbas, Abbas Afsharfarnia, Faraneh Zarafshan, and S. A. R. Al-Haddad. "A Novel Clustering Algorithm for Mobile Ad Hoc Networks Based on Determination of Virtual Links’ Weight to Increase Network Stability." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/432952.

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The stability of clusters is a serious issue in mobile ad hoc networks. Low stability of clusters may lead to rapid failure of clusters, high energy consumption for reclustering, and decrease in the overall network stability in mobile ad hoc network. In order to improve the stability of clusters, weight-based clustering algorithms are utilized. However, these algorithms only use limited features of the nodes. Thus, they decrease the weight accuracy in determining node’s competency and lead to incorrect selection of cluster heads. A new weight-based algorithm presented in this paper not only de
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Nowlan, Steven J., and Geoffrey E. Hinton. "Simplifying Neural Networks by Soft Weight-Sharing." Neural Computation 4, no. 4 (1992): 473–93. http://dx.doi.org/10.1162/neco.1992.4.4.473.

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One way of simplifying neural networks so they generalize better is to add an extra term to the error function that will penalize complexity. Simple versions of this approach include penalizing the sum of the squares of the weights or penalizing the number of nonzero weights. We propose a more complicated penalty term in which the distribution of weight values is modeled as a mixture of multiple gaussians. A set of weights is simple if the weights have high probability density under the mixture model. This can be achieved by clustering the weights into subsets with the weights in each cluster
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Reynolds, Andrew G., and Christiane de Savigny. "Influence of Girdling and Gibberellic Acid on Yield Components, Fruit Composition, and Vestigial Seed Formation of `Sovereign Coronation' Table Grapes." HortScience 39, no. 3 (2004): 541–44. http://dx.doi.org/10.21273/hortsci.39.3.541.

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Vestigial seeds of `Sovereign Coronation' table grapes frequently form partial seedcoats that are perceptible during consumption. This problem was addressed through cane/cordon girdling and gibberellic acid (GA3) sprays. `Sovereign Coronation' vines were subjected to one of five treatments [untreated control; cane/cordon girdled; 15 ppm GA3 at bloom (GA1); GA1 + 40 ppm GA3 14 days later (GA2); GA2 + 40 ppm GA3 14 days later]. GA3 had no effect on yield or clusters per vine, but postbloom GA3 treatments increased cluster and berry weights and reduced berries per cluster. Fruit maturity was not
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Harksoo Kim and Jungyun Seo. "Cluster-Based FAQ Retrieval Using Latent Term Weights." IEEE Intelligent Systems 23, no. 2 (2008): 58–65. http://dx.doi.org/10.1109/mis.2008.23.

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Dissertations / Theses on the topic "Cluster weights"

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Al-Razgan, Muna Saleh. "Weighted clustering ensembles." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3212.

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Thesis (Ph.D.)--George Mason University, 2008.<br>Vita: p. 134. Thesis director: Carlotta Domeniconi. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed Oct. 14, 2008). Includes bibliographical references (p. 128-133). Also issued in print.
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Zhu, Tao. "Extended cluster weighted modeling methods for transient recognition control." Diss., Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/zhu/ZhuT0806.pdf.

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Lyman, Mark B. "A modified cluster-weighted approach to nonlinear time series /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1945.pdf.

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Lyman, Mark Ballatore. "A Modified Cluster-Weighted Approach to Nonlinear Time Series." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1170.

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In many applications involving data collected over time, it is important to get timely estimates and adjustments of the parameters associated with a dynamic model. When the dynamics of the model must be updated, time and computational simplicity are important issues. When the dynamic system is not linear the problem of adaptation and response to feedback are exacerbated. A linear approximation of the process at various levels or “states” may approximate the non-linear system. In this case the approximation is linear within a state and transitions from state to state over time. The transition p
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Chan, Yat-ling, and 陳逸靈. "An optimization algorithm for clustering using weighted dissimilarity measures." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B26667009.

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Yan, Mingjin. "Methods of Determining the Number of Clusters in a Data Set and a New Clustering Criterion." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/29957.

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In cluster analysis, a fundamental problem is to determine the best estimate of the number of clusters, which has a deterministic effect on the clustering results. However, a limitation in current applications is that no convincingly acceptable solution to the best-number-of-clusters problem is available due to high complexity of real data sets. In this dissertation, we tackle this problem of estimating the number of clusters, which is particularly oriented at processing very complicated data which may contain multiple types of cluster structure. Two new methods of choosing the number of clust
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Seligman, Luiz Carlos. "Macrossomia no Brasil : tendências temporais e epidemiologia espacial." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/12054.

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Macrossomia fetal significa feto grande ou com sobrepeso, mais recentemente chamado de recém-nascido grande para idade gestacional. Diversos fatores afetam a distribuição do peso corporal fetal tais como a idade gestacional, tamanho materno, hereditariedade, estado socioeconômico, origem étnica entre tantos outros. Conseqüentemente, observa-se uma morbidade aumentada nesta situação. A tendência temporal da macrossomia foi avaliada em estudos realizados em outros países e mostrou aumento gradativo de sua prevalência, além de uma distribuição geográfica heterogênea. Objetivos: Avaliar a tendênci
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Ferreira, Manuela Klanovicz. "Mapeamento estático de processos MPI com emparelhamento perfeito de custo máximo em cluster homogêneo de multi-cores." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/65636.

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Um importante fator que precisa ser considerado para alcançar alto desempenho em aplicações paralelas é a distribuição dos processos nos núcleos do sistema, denominada mapeamento de processos. Mesmo o mapeamento estático de processos é um problema NP-difícil. Por esse motivo, são utilizadas heurísticas que dependem da aplicação e do hardware no qual a aplicação será mapeada. Nas arquiteturas atuais, além da possibilidade de haver mais de um processador por nó do cluster, é possível haver mais de um núcleo de processamento por processador, assim, o mapeamento estático de processos pode consider
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Thickstun, Charles Russell. "Spatial Variation in Risk Factors for Malaria in Muleba, Tanzania." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39082.

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Despite the rich knowledge surrounding risk factors for malaria, the spatial processes of malaria transmission and vector control interventions are underexplored. This thesis aims 1) to describe the spatial variation of risk factor effects on malaria infection, and 2) to determine the presence and range of any community effect from malaria vector control interventions. Data from a cluster-randomized control trial in Tanzania were analyzed to determine the geographically-weighted odds of malaria infection in children at trial baseline and post-intervention. The spatial range of intervention eff
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Armbruster, Michael. "Branch-and-Cut for a Semidefinite Relaxation of Large-scale Minimum Bisection Problems." Doctoral thesis, Universitätsbibliothek Chemnitz, 2007. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200701057.

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This thesis deals with the exact solution of large-scale minimum bisection problems via a semidefinite relaxation in a branch-and-cut framework. After reviewing known results on the underlying bisection cut polytope a study of new facet-defining inequalities is presented. They are derived from the known knapsack tree inequalities. We investigate strengthenings based on the new cluster weight polytope and present polynomial separation algorithms for special cases. The dual of the semidefinite relaxation of the minimum bisection problem is tackled in its equivalent form as an eigenvalue optimisa
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Books on the topic "Cluster weights"

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Miksza, Peter, and Kenneth Elpus. Analyses of Data from Complex Survey Sampling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0011.

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This chapter introduces the specialized techniques necessary for analyzing data that have been gathered in a complex or multistage survey sample. The chapter details the methods most commonly used to collect complex survey data and then explains the specific statistical tools that must be employed to correctly analyze complex survey data. First, an overview of the various types of sampling methods is presented, beginning with simple random sampling and moving through other methods to finally discuss the commonly employed research techniques of cluster sampling. The chapter continues with a dis
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Topintzi, Nina, and Stuart Davis. On the weight of edge geminates. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198754930.003.0012.

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This chapter focuses on edge geminates (EGs), which, compared to intervocalic geminates, are rarer and potentially structurally different. An initial typology of the weight properties of EGs is presented and observations are made that may predict whether an EG patterns as heavy or light. Moreover, the relationship and possible correlations between EGs and edge consonant clusters in the languages under consideration are explored. An initial finding suggests that if EGs are unique in a language, i.e. the language lacks edge clusters, then the geminate is more likely to pattern as moraic (cf. Tru
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Horing, Norman J. Morgenstern. Non-Equilibrium Green’s Functions: Variational Relations and Approximations for Particle Interactions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198791942.003.0009.

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Chapter 09 Nonequilibrium Green’s functions (NEGF), including coupled-correlated (C) single- and multi-particle Green’s functions, are defined as averages weighted with the time-development operator U(t0+τ,t0). Linear conductivity is exhibited as a two-particle equilibrium Green’s function (Kubo-type formulation). Admitting particle sources (S:η,η+) and non-conservation of number, the non-equilibrium multi-particle Green’s functions are constructed with numbers of creation and annihilation operators that may differ, and they may be derived as variational derivatives with respect to sources η,η
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Book chapters on the topic "Cluster weights"

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Faußer, Stefan, and Friedhelm Schwenker. "Semi-Supervised Kernel Clustering with Sample-to-Cluster Weights." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28258-4_8.

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Alfó, Marco, Luciano Nieddu, and Cecilia Vitiello. "Cluster Weighted Beta Regression: A Simulation Study." In Statistical Learning of Complex Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21140-0_1.

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Punzo, Antonio, and Salvatore Ingrassia. "Parsimonious Generalized Linear Gaussian Cluster-Weighted Models." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17377-1_21.

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Carcassoni, Marco, and Edwin R. Hancock. "Weighted Graph-Matching Using Modal Clusters." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44692-3_18.

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van Heerden, Willem S., and Andries P. Engelbrecht. "Unsupervised Weight-Based Cluster Labeling for Self-Organizing Maps." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35230-0_5.

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van Stein, Bas, Hao Wang, Wojtek Kowalczyk, Thomas Bäck, and Michael Emmerich. "Optimally Weighted Cluster Kriging for Big Data Regression." In Advances in Intelligent Data Analysis XIV. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24465-5_27.

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Vega-Pons, Sandro, Jyrko Correa-Morris, and José Ruiz-Shulcloper. "Weighted Cluster Ensemble Using a Kernel Consensus Function." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85920-8_24.

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Lin, I.-Chun, and Cheng-Yuan Liou. "Least-Mean-Square Training of Cluster-Weighted Modeling." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74695-9_31.

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Chen, Zhenzhou. "Kernel Generalized Foley-Sammon Transform with Cluster-Weighted." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74205-0_94.

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Hotta, Seiji, Kohei Inoue, and Kiichi Urahama. "Extraction of Fuzzy Clusters from Weighted Graphs." In Knowledge Discovery and Data Mining. Current Issues and New Applications. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45571-x_51.

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Conference papers on the topic "Cluster weights"

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Dey, Sayak, Swagatam Das, and Rammohan Mallipeddi. "The Sparse MinMax k-Means Algorithm for High-Dimensional Clustering." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/291.

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Classical clustering methods usually face tough challenges when we have a larger set of features compared to the number of items to be partitioned. We propose a Sparse MinMax k-Means Clustering approach by reformulating the objective of the MinMax k-Means algorithm (a variation of classical k-Means that minimizes the maximum intra-cluster variance instead of the sum of intra-cluster variances), into a new weighted between-cluster sum of squares (BCSS) form. We impose sparse regularization on these weights to make it suitable for high-dimensional clustering. We seek to use the advantages of the
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Yang, Hao, Yuan Dong, Xianyu Zhao, Jian Zhao, Liang Lu, and Haila Wang. "Cluster adaptive training weights as features in SVM-based speaker verification." In Interspeech 2007. ISCA, 2007. http://dx.doi.org/10.21437/interspeech.2007-163.

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Zhang, Keliang, and Baifeng Wu. "Task Scheduling Greedy Heuristics for GPU Heterogeneous Cluster Involving the Weights of the Processor." In 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW). IEEE, 2013. http://dx.doi.org/10.1109/ipdpsw.2013.38.

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Nie, Feiping, Jing Li, and Xuelong Li. "Self-weighted Multiview Clustering with Multiple Graphs." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/357.

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In multiview learning, it is essential to assign a reasonable weight to each view according to its importance. Thus, for multiview clustering task, a wise and elegant method should achieve clustering multiview data while learning the view weights. In this paper, we address this problem by exploring a Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each view with different confidences. We start our work with a natural thought that the weights can be learned by introducing a hyperparameter. By analyzing the weakness of it, we further propose a
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Lin, Mei, Zhong-hua Wang, Cheng-wu Zou, and Min Yu. "Double Cluster-Heads Routing Policy Based on the Weights of Energy-Efficient for Wireless Sensor Networks." In 2010 International Conference on Computational and Information Sciences (ICCIS). IEEE, 2010. http://dx.doi.org/10.1109/iccis.2010.173.

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Kang, Zhao, Zipeng Guo, Shudong Huang, et al. "Multiple Partitions Aligned Clustering." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/375.

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Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie. Such common practice may cause significant information loss because of unavoidable noise or inconsistency among views. Since different views admit the same cluster structure, the natural space should be all partitions. Orthogonal to existing techniques, in this paper, we propose to leverage the multi-view information by fusing partit
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Zingaretti, Primo, Andrea Ascani, Adriano Mancini, and Emanuele Frontoni. "Particle Clustering to Improve Omnidirectional Localization in Outdoor Environments." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87373.

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Monte Carlo Localization (MCL) is a common method for self-localization of a mobile robot under the assumption that a map of the environment is available. In addition to laser scanners and sonar sensors, localization approaches using vision sensors have also been recently developed with good results. In this paper we present two variations to improve the standard implementation of the MCL algorithm. The first change consists in a new strategy for the generation of particles, both at the initialization and at the resampling stage, which tries to generate new particles near the position of image
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Bekti, Rokhana Dwi, Gideon Eka Dirgantara, and Edhy Sutanta. "Distance and AMOEBA Weights Matrices in Local Getis Ord-G Statistics to Identify Spatial Cluster of Gini Ratio." In 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA). IEEE, 2021. http://dx.doi.org/10.1109/icera53111.2021.9538666.

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Wang, Xiao, Shaohua Fan, Kun Kuang, Chuan Shi, Jiawei Liu, and Bai Wang. "Decorrelated Clustering with Data Selection Bias." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/301.

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Most of existing clustering algorithms are proposed without considering the selection bias in data. In many real applications, however, one cannot guarantee the data is unbiased. Selection bias might bring the unexpected correlation between features and ignoring those unexpected correlations will hurt the performance of clustering algorithms. Therefore, how to remove those unexpected correlations induced by selection bias is extremely important yet largely unexplored for clustering. In this paper, we propose a novel Decorrelation regularized K-Means algorithm (DCKM) for clustering with data se
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Ren, Pengzhen, Yun Xiao, Pengfei Xu, et al. "Robust Auto-Weighted Multi-View Clustering." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/367.

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Multi-view clustering has played a vital role in real-world applications. It aims to cluster the data points into different groups by exploring complementary information of multi-view. A major challenge of this problem is how to learn the explicit cluster structure with multiple views when there is considerable noise. To solve this challenging problem, we propose a novel Robust Auto-weighted Multi-view Clustering (RAMC), which aims to learn an optimal graph with exactly k connected components, where k is the number of clusters. ℓ1-norm is employed for robustness of the proposed algorithm. We h
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Reports on the topic "Cluster weights"

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Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.79.

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algo
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Winans, R. E. ,., Y. Kim, J. E. Hunt, and R. L. McBeth. Structural elucidation of Argonne premium coals: Molecular weights, heteroatom distributions and linkages between clusters. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/206361.

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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 sam
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