Results 51 to 60 of about 1,195,838 (307)
Temporal Hierarchical Clustering
CoRR, 2017 14 pages, 4 ...
Dey, Tamal K. +2 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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Divisive hierarchical maximum likelihood clustering
BMC Bioinformatics, 2017 Background Biological data comprises various topologies or a mixture of forms, which makes its analysis extremely complicated. With this data increasing in a daily basis, the design and development of efficient and accurate statistical methods has become
Alok Sharma +2 more
doaj +1 more source
Hierarchical Clustering: Objective Functions and Algorithms [PDF]
Journal of the ACM, 2018 Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization ...
Vincent Cohen-Addad +3 more
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Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering
Information, 2018 Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains.
Hamzeh Eyal Salman +3 more
doaj +1 more source
From mice to humans—divergent strategies for intestinal homeostasis and regeneration
FEBS Letters, EarlyView.Recent advances such as organoid genome editing, xenotransplantation, imaging, and whole‐genome sequencing have enabled direct studies of human intestinal stem cells (ISCs). These studies reveal species‐specific features, including slower ISC proliferation, distinct injury responses, slower somatic mutation accumulation in humans, and an inverse ...
Keiko Ishikawa +2 more
wiley +1 more source
Autonomous clustering using rough set theory [PDF]
, 2008 This paper proposes a clustering technique that minimises the need for subjective human intervention and is based on elements of rough set theory. The proposed algorithm is unified in its approach to clustering and makes use of both local and global ...
Chandra Kambhampati +3 more
core +1 more source
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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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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Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry
FEBS Letters, EarlyView.EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri +5 more
wiley +1 more source