Results 211 to 220 of about 858,679 (268)
Identifying Risk Groups in 401,846 Osteoarthritis Patients Undergoing Total Hip Arthroplasty: A Machine Learning Clustering Analysis. [PDF]
J Pers MedAhmadi A +7 more
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GOLDEN fusion: a graph-oriented learning with domain-embedding network fusion for generating super gene sets in functional genomics. [PDF]
Brief BioinformLi Q +5 more
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A Reliable and Secure Cluster-Routing Framework for Drone-Assisted Disaster Management in Smart Cities. [PDF]
Sensors (Basel)Alwasel B +4 more
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Correction: Classification of fielders in nippon professional baseball using a Gaussian mixture clustering model. [PDF]
Front Sports Act LivingOda T, Hirotsu N.
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Pattern Recognition, 1992
Abstract An algorithm for the clustering of existing clusters is introduced in this paper. The algorithm was adopted from fuzzy-c-mean and modifications made to take into account the extra information, i.e. some data samples already form clusters. Partition coefficients, together with some other criteria, are used for testing cluster validity.
Cheung, YS, Chan, KP
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Abstract An algorithm for the clustering of existing clusters is introduced in this paper. The algorithm was adopted from fuzzy-c-mean and modifications made to take into account the extra information, i.e. some data samples already form clusters. Partition coefficients, together with some other criteria, are used for testing cluster validity.
Cheung, YS, Chan, KP
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Science, 2007
The PDZ domain is a frequently occurring interaction domain in eukaryotic proteins that binds to the C termini of target proteins. PDZ domains have been thought to cluster into functional classes, each with distinct sequence binding preferences. Using a combination of experimental data and modeling, Stiffler et al .
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The PDZ domain is a frequently occurring interaction domain in eukaryotic proteins that binds to the C termini of target proteins. PDZ domains have been thought to cluster into functional classes, each with distinct sequence binding preferences. Using a combination of experimental data and modeling, Stiffler et al .
openaire +2 more sources
Incremental Clustering for Hierarchical Clustering
2018 5th International Conference on Computational Science/ Intelligence and Applied Informatics (CSII), 2018This paper proposes a clustering algorithm for updating clusters without reclustering when a point is inserted. We define the center and the radius of the cluster, and update clustering results of points using them. We introduce the concept of outliers and also consider the change in the number of clusters caused by data insertion.
Kakeru Narita +2 more
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2009
In this chapter, we shall show several spatial point patterns in a hypothetical area of a square 10 km on each side to understand what “clustering” or “a cluster” means and to get a basic idea of how to approach detection of clustering or clusters.
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In this chapter, we shall show several spatial point patterns in a hypothetical area of a square 10 km on each side to understand what “clustering” or “a cluster” means and to get a basic idea of how to approach detection of clustering or clusters.
openaire +1 more source