Results 231 to 240 of about 761,172 (262)

EMO-PEGASIS: A Dual-Phase Machine Learning Protocol for Energy Delay Optimisation in WSNs. [PDF]

Sensors (Basel)
Juwaied A.
europepmc   +1 more source

Fabrication Process and Particle Dispersion Characteristics of W-PETG-Based 3D-Printed Composites for Medical Radiation Shielding. [PDF]

Polymers (Basel)
Kim SC.
europepmc   +1 more source

High-speed 3D DNA PAINT and unsupervised clustering for unlocking 3D DNA origami cryptography. [PDF]

Nat Commun
Wisna GBM, Sukhareva D, Zhao J, Chopade P, Satyabola D, Matthies M, Roy S, Wang C, Šulc P, Yan H, Hariadi RF.   +10 more
europepmc   +1 more source

RF model and SNA analysis for optimization of regional financial supervision system. [PDF]

Sci Rep
Wei T.
europepmc   +1 more source

Adaptive density peak clustering based on Delaunay graph

PLoS ONE
Wei Xingqiong, Li Kang
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Density‐based clustering

WIREs Data Mining and Knowledge Discovery, 2011
Abstract Clustering refers to the task of identifying groups or clusters in a data set. In density‐based clustering , a cluster is a set of data objects spread in the data space over a contiguous region of high density of objects.
Hans‐Peter Kriegel, Peer Kröger, Jörg Sander, Arthur Zimek   +3 more
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Active Density-Based Clustering

2013 IEEE 13th International Conference on Data Mining, 2013
The density-based clustering algorithm DBSCAN is a fundamental technique for data clustering with many attractive properties and applications. However, DBSCAN requires specifying all pair wise (dis)similarities among objects that can be non-trivial to obtain in many applications.
Mai, S. T., He, X., Hubig, N., Plant, Claudia, Böhm, C.   +4 more
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Data density based clustering

2014 14th UK Workshop on Computational Intelligence (UKCI), 2014
A new, data density based approach to clustering is presented which automatically determines the number of clusters. By using RDE for each data sample the number of calculations is significantly reduced in offline mode and, further, the method is suitable for online use.
Hyde, Richard, Angelov, Plamen
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Privacy-preserving Density-based Clustering

Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security, 2021
Clustering is an unsupervised machine learning technique that outputs clusters containing similar data items. In this work, we investigate privacy-preserving density-based clustering which is, for example, used in financial analytics and medical diagnosis. When (multiple) data owners collaborate or outsource the computation, privacy concerns arise.
Bozdemir, Beyza, Canard, Sébastien, Ermis, Orhan, Möllering, Helen, Önen, Melek, Schneider, Thomas   +5 more
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