Results 241 to 250 of about 866,547 (282)

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
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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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Parallel clustering algorithms

Parallel Computing, 1989
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Xiaobo, Fang, Zhixi
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Clustering Algorithms.

The Statistician, 1976
M. G. Kendall, J. A. Hartigan
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Clusters Analyzer Algorithm for Informative Acquaintances - Quantum Clustering Algorithm

2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), 2020
In this internet and digitalization age, information in any form is very important to perform a digital task. The processing of any information to obtain the desired results requires a specific medium and sometimes to accomplish various tasks related to that set of information like browsing, searching, sorting, retrieval and management.
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Clustering I: Basic Clustering Models and Algorithms

2013
Clustering is a fundamental tool for data analysis. It finds wide applications in many engineering and scientific fields including pattern recognition, feature extraction, vector quantization, image segmentation, bioinformatics, and data mining. Clustering is a classical method for the prototype selection of kernel-based neural networks such as the RBF
Ke-Lin Du, M. N. S. Swamy
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A Robust Information Clustering Algorithm

Neural Computation, 2005
We focus on the scenario of robust information clustering (RIC) based on the minimax optimization of mutual information (MI). The minimization of MI leads to the standard mass-constrained deterministic annealing clustering, which is an empirical risk-minimization algorithm.
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