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Super Atomic Clusters: Design Rules and Potential for Building Blocks of Materials
Chemical Reviews, 2018Atomic clusters, consisting of a few to a few thousand atoms, have emerged over the past 40 years as the ultimate nanoparticles, whose structure and properties can be controlled one atom at a time. One of the early motivations in studying clusters was to
P Jena, Qiang Sun
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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 .
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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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Maximum Clusterability Divisive Clustering
2015 IEEE Symposium Series on Computational Intelligence, 2015The notion of cluster ability is often used to determine how strong the cluster structure within a set of data is, as well as to assess the quality of a clustering model. In multivariate applications, however, the cluster ability of a data set can be obscured by irrelevant or noisy features.
David P. Hofmeyr, Nicos G. Pavlidis
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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.
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To Cluster or Not Cluster, That is the Question
The American Journal of Geriatric Psychiatry, 2022Roy, Adams, Paul, Rosenberg
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Dynamic cluster-size distribution in cluster-cluster aggregation: Effects of cluster diffusivity
Physical Review B, 1985The dynamics of the diffusion-limited model of cluster-cluster aggregation is investigated in two and three dimensions by studying the temporal evolution of the cluster-size distribution ${n}_{s}$(t), which is the number of clusters of size s at time t.
, Meakin, , Vicsek, , Family
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