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Colloidal Heterostructures Enable Interfacial Transport of Immiscible Molecules in Printable Organohydrogels

Advanced Materials, EarlyView.
Multiphase printable organohydrogels with tunable microstructures are developed to control molecular transport pathways for immiscible cargo. The tortuosity and domain size of the colloidal phases are tuned by adjusting temperature and shear during processing, which enables the tailoring of diffusion kinetics due to different transport pathways.
Riley E. Dowdy‐Green, Rony Waheibi, Nazanin Shakoury, Yuemei Ye, Lilian C. Hsiao   +4 more
wiley   +1 more source

Clustering Algorithms: Their Application to Gene Expression Data

Bioinformatics and Biology Insights, 2016
Jelili Oyelade, Itunuoluwa Isewon, Funke Oladipupo, Olufemi Aromolaran, Efosa Uwoghiren, Faridah Ameh, Moses Achas, Ezekiel Adebiyi   +7 more
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Fast Clustering Algorithms

ORSA Journal on Computing, 1994
This paper considers the problem of partitioning the vertices of a weighted complete graph into cliques of unbounded size and number, such that the sum of the edge weights of all cliques is maximized. The problem is known as the clique-partitioning problem and arises as a clustering problem in qualitative data analysis.
Dorndorf, Ulrich, Pesch, Erwin
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Genetic clustering algorithms

European Journal of Operational Research, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chiou, Yu-Chiun, Lan, Lawrence W.
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An Efficient Clustering Algorithm

IEEE Transactions on Systems, Man, and Cybernetics, 1976
A new algorithm is presented (called matching algorithm), in order to reorganize data data and identify clusters. Thus the algorithm requires only three integer additions in each step, instead of comparing the entire row to evaluate the matchings with the neighboring rows.
Bhat, M. V., Haupt, A.
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Clustering Criteria and Algorithms

2009
Cluster analysis is an unsupervised pattern recognition frequently used in biology, where large amounts of data must often be classified. Hierarchical agglomerative approaches, the most commonly used techniques in biology, are described in this chapter.
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Improved Clustering Algorithm Based on AOW Clustering Algorithm

2019 7th International Conference on Information, Communication and Networks (ICICN), 2019
Clustering algorithms play an important role in wireless self-organized network, especially the adaptive on-demand weighted clustering algorithm (AOW). But the AOW algorithm also has some limitations in most application scenarios. In this paper, combined the military activities, we introduce and analyze the AOW algorithm’s three shortcomings.
Zhen Wang, Jianguo Yu, Lan Wang, Zhifang Wang   +3 more
openaire   +1 more source

CLUSTER ALGORITHMS FOR SURFACES

International Journal of Modern Physics C, 1992
We discuss a new cluster algorithm that completely eliminates critical slowing down for surface models of the SOS (solid-on-solid) type.
Evertz, Hans Gerd, Hasenbusch, Martin, Marcu, Mihail, Pinn, Klaus, Solomon, Sorin   +4 more
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Ultrametric Hierarchical Clustering Algorithms

Psychometrika, 1979
Johnson has shown that the single linkage and the complete linkage hierarchical clustering algorithms induce a metric on the data known as the ultrametric. Through the use of the Lance and Williams recurrence formula, Johnson's proof is extended to four other common clustering algorithms.
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Robust MST-Based Clustering Algorithm

Neural Computation, 2018
Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle:
Liu, Qidong, Zhang, Ruisheng, Zhao, Zhili, Wang, Zhenghai, Jiao, Mengyao, Wang, Guangjing   +5 more
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