Results 281 to 290 of about 1,206,915 (318)

Correlation Clustering and Consensus Clustering

2005
The Correlation Clustering problem has been introduced recently [5] as a model for clustering data when a binary relationship between data points is known. More precisely, for each pair of points we have two scores measuring respectively the similarity and dissimilarity of the two points, and we would like to compute an optimal partition where the ...
Paola Bonizzoni, Gianluca Della Vedova, Riccardo Dondi, Tao Jiang 0001   +3 more
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Clustering and Clusters

1978
When one is lecturing in a centre for interdisciplinary research, it is, I imagine, appropriate to start by emphasizing the interdisciplinary character of the topic one is going to discuss. In my case this does not present any difficulty. Clustering is a pictorial term for positive correlations in an ensemble of randomly distributed bodies or events ...
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Fuzzy clustering: Determining the number of clusters

2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN), 2012
In this study we analyze behavior of two types of coefficients for determining the suitable number of clusters obtained when fuzzy cluster analysis is applied. First one is Dunn's coefficient which contains membership degrees in its computational formula; second one is the average silhouette width, used primarily for evaluating hard clustering.
Hana Rezanková, Dusan Húsek
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Cluster headache and cluster variants

Current Treatment Options in Neurology, 2003
Patients must be cognizant of the time course of the cluster headache periods to optimally tailor their therapy. Steroids provide the fastest onset of prophylactic effect. Once steroids are initiated, it remains difficult to wean patients off of them, and that is why it is always recommended to associate another prophylactic agent from the onset with ...
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On the Number of Clusters in Cluster Analysis

1998
Clustering is a fundamental tool for analyzing the structure of feature spaces. It has been applied to various fields such as pattern recognition, information retrieval and so on. Many studies have been done on this problem and various kinds of clustering methods have been proposed and compared (e.g., [1]).
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Joint Cluster Based Co-clustering for Clustering Ensembles

2006
This paper introduces a new method for solving clustering ensembles, that is, combining multiple clusterings over a common dataset into a final better one. The ensemble is reduced to a graph that simultaneously models as vertices the original clusters in the ensemble and the joint clusters derived from them.
Tianming Hu, Liping Liu, Chao Qu, Sam Yuan Sung   +3 more
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CLUSTERS AND CLUSTERS OF CLUSTERS IN COLLISIONS

Photonic, Electronic and Atomic Collisions, 2006
B. MANIL, V. BERNIGAUD, P. BODUCH, A. CASSIMI, O. KAMALOU, J. LENOIR, L. MAUNOURY, J. RANGAMA, B. A. HUBER, J. JENSEN, H. T. SCHMIDT, H. ZETTERGREN, H. CEDERQUIST, S. TOMITA, P. HVELPLUND, F. ALVARADO, S. BARI, A. LECOINTRE, T. SCHLATHÖLTER   +18 more
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Data clustering: application and trends

Artificial Intelligence Review, 2022
Gbeminiyi John Oyewole, George Alex Thopil   +1 more
exaly  

An Ensemble Clustering Framework Based on Hierarchical Clustering Ensemble Selection and Clusters Clustering

Cybernetics and Systems, 2022
Wenjun Li, Zikang Wang, Wei Sun, Sara Bahrami   +3 more
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To Cluster or not to Cluster: A VRU Clustering Based on V2X Communication

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023
Silas C. Lobo, Leonardo Barbosa da Silva, Christian Facchi   +2 more
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