Results 11 to 20 of about 216,194 (219)
Temporal Hierarchical Clustering
CoRR, 2017 14 pages, 4 ...
Dey, Tamal K. +2 more
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Scalable adaptive hierarchical clustering [PDF]
IEEE Communications Letters, 2002 We propose a new application-level clustering algorithm capable of building an overlay spanning tree among participants of large multicast sessions, without any specific help from the network routers. This algorithm is based on a unique definition of zones around nodes and an innovative adaptive cluster size distribution.
MATHY L. +3 more
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Hierarchical clustering of asymmetric networks [PDF]
Advances in Data Analysis and Classification, 2017 arXiv admin note: substantial text overlap with arXiv:1301 ...
Gunnar E. Carlsson +3 more
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Likelihood Based Hierarchical Clustering [PDF]
IEEE Transactions on Signal Processing, 2004 This paper develops a new method for hierarchical clustering. Unlike other existing clustering schemes, our method is based on a generative, tree-structured model that represents relationships between the objects to be clustered, rather than directly modeling properties of objects themselves.
Castro, R.M., Coates, M., Nowak, R.
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Hierarchical clustering of words [PDF]
Proceedings of the 16th conference on Computational linguistics -, 1996 This paper describes a data-driven method for hierarchical clustering of words in which a large vocabulary of English words is clustered bottom-up, with respect to corpora ranging in size from 5 to 50 million words, using a greedy algorithm that tries to minimize average loss of mutual information of adjacent classes.
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HIERARCHICAL CLUSTERING AND THE BAO SIGNATURE [PDF]
The Thirteenth Marcel Grossmann Meeting, 2015 3 pages, 1 figure, Proceedings of the 13th Marcel Grossman Meeting on General Relativity (Stockholm, Sweden, July 1 - 7, 2012), invited talk at the Parallel Session OC4 "New developments in the study of the large scale structure of the Universe"
Hellwing, Wojciech A. +3 more
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The Price of Hierarchical Clustering
AlgorithmicaAbstract Hierarchical Clustering is a popular tool for understanding the hereditary properties of a data set. Such a clustering is actually a sequence of clusterings that starts with the trivial clustering in which every data point forms its own cluster and then successively merges two existing clusters until all points are in the same ...
Anna Arutyunova, Heiko Röglin
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Information Theoretic Hierarchical Clustering [PDF]
Entropy, 2011 Hierarchical clustering has been extensively used in practice, where clusters can be assigned and analyzed simultaneously, especially when estimating the number of clusters is challenging. However, due to the conventional proximity measures recruited in these algorithms, they are only capable of detecting mass-shape clusters and encounter problems in ...
Mehdi Aghagolzadeh +2 more
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The ubiquitin‐proteasome system and autophagy as guardians of the cellular proteome
FEBS Letters, EarlyView.This Perspective covers the three principles governing the crosstalk between the ubiquitin‐proteasome system and autophagy in cellular proteostasis: (1) a shared ubiquitin code routing substrates via shuttle factors or autophagy receptors; (2) spatial compartmentalization into phase‐separated degradation hubs and organelle‐specific modules (exemplified
Ivan Dikic
wiley +1 more source
Hierarchical kernel spectral clustering [PDF]
Neural Networks, 2012 Kernel spectral clustering fits in a constrained optimization framework where the primal problem is expressed in terms of high-dimensional feature maps and the dual problem is expressed in terms of kernel evaluations. An eigenvalue problem is solved at the training stage and projections onto the eigenvectors constitute the clustering model.
Alzate Perez, Carlos, Suykens, Johan
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