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Clustering cluster algebras with clusters
Advances in Theoretical and Mathematical Physics, 2023 Classification of cluster variables in cluster algebras (in particular, Grassmannian cluster algebras) is an important problem, which has direct application to computations of scattering amplitudes in physics. In this paper, we apply the tableaux method to classify cluster variables in Grassmannian cluster algebras $\mathbb{C}[Gr(k,n)]$ up to $(k,n)=(3,
Cheung, MW +5 more
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Persistence in cluster-cluster aggregation [PDF]
Physical Review E, 2002 14 pages, 12 figures, RevTeX, submitted to Phys.
Hellén, E.K.O., Alava, M.J.
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, 2008 Clustering techniques are used to arrange genes in some natural way, that is, to organize genes into groups or clusters with similar behavior across relevant tissue samples (or cell lines). These techniques can also be applied to tissues rather than genes.
McLachlan, G. J., Bean, R. W., Ng, S. K.
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The Astrophysical Journal, 1985 The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models.
J. Barnes +3 more
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Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions
Frontiers in Neuroscience, 2019 Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic ...
Vincent Grollemund +15 more
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SOTXTSTREAM: Density-based self-organizing clustering of text streams [PDF]
, 2017 A streaming data clustering algorithm is presented building upon the density-based selforganizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous ...
Bryant, Avory C., Cios, Krzysztof J.
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Application of Multivariate-Rank-Based Techniques in Clustering of Big Data
Vikalpa, 2018 Executive Summary Very large or complex data sets, which are difficult to process or analyse using traditional data handling techniques, are usually referred to as big data.
Pritha Guha
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Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
Applied Sciences, 2019 With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems.
Yifeng Zhao +4 more
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Self-adaptive GA, quantitative semantic similarity measures and ontology-based text clustering [PDF]
, 2008 As the common clustering algorithms use vector space model (VSM) to represent document, the conceptual relationships between related terms which do not co-occur literally are ignored.
Li, Chenghua +3 more
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Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
Algorithms, 2017 We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online.
Xin Tian, Song Li
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