Hierarchical Clustering of Large-Scale Short Conversations Based on Domain Ontology

2008 ◽  
Author(s):  
Yongheng Wang ◽  
Bo Guo
Keyword(s):  
Hierarchical Clustering ◽  
Large Scale ◽  
Domain Ontology

scholarly journals Self-organizing Clustering: Non-hierarchical Clustering for Large Scale DNA Sequence Data

2007 ◽  
Vol 3 ◽  
pp. 193-197 ◽  
Author(s):  
Kou Amano ◽  
Hiroaki Ichikawa ◽  
Hidemitsu Nakamura ◽  
Hisataka Numa ◽  
Kaoru Fukami-Kobayashi ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Hierarchical Clustering ◽  
Large Scale ◽  
Sequence Data ◽  
Dna Sequence Data ◽  
Self Organizing

The Cloud@Home Volunteer and Interoperable Cloud through the Future Internet

2012 ◽  
pp. 79-96
Author(s):  
Salvatore Distefano ◽  
Antonio Puliafito
Keyword(s):  
Cloud Computing ◽  
Business Model ◽  
Hierarchical Clustering ◽  
Large Scale ◽  
Future Internet ◽  
Cloud Infrastructure ◽  
The Future ◽  
Service Oriented

Cloud computing is the new consolidated trend in ICT, often considered as the panacea to all the problems of existing large-scale distributed paradigms such as Grid and hierarchical clustering. The Cloud breakthrough is the service oriented perspective of providing everything “as a service”. Different from the others large-scale distributed paradigms, it was born from commercial contexts, with the aim of selling the temporarily unexploited computing resources of huge datacenters in order to reduce the costs. Since this business model is really attractive and convenient for both providers and consumers, the Cloud paradigm is quickly growing and widely spreading, even in non commercial context. In fact, several activities on the Cloud, such as Nimbus, Eucalyptus, OpenNEbula, and Reservoir, etc., have been undertaken, aiming at specifying open Cloud infrastructure middleware.

scholarly journals Evaluation by hierarchical clustering of multiple cytokine expression after phytohemagglutinin stimulation

2016 ◽  
Vol 68 (3) ◽  
pp. 509-513 ◽  
Author(s):  
Chunhe Yang ◽  
Hongwu Du
Keyword(s):  
Hierarchical Clustering ◽  
Large Scale ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Cytokine Expression ◽  
Biological Data ◽  
Clustering Method ◽  
Data Set ◽  
Suspension Array ◽  
Pha Stimulation

The hierarchical clustering method has been used for exploration of gene expression and proteomic profiles; however, little research into its application in the examination of expression of multiplecytokine/chemokine responses to stimuli has been reported. Thus, little progress has been made on how phytohemagglutinin(PHA) affects cytokine expression profiling on a large scale in the human hematological system. To investigate the characteristic expression pattern under PHA stimulation, Luminex, a multiplex bead-based suspension array, was performed. The data set collected from human peripheral blood mononuclear cells (PBMC) was analyzed using the hierarchical clustering method. It was revealed that two specific chemokines (CCL3 andCCL4) underwent significantly greater quantitative changes during induction of expression than other tested cytokines/chemokines after PHA stimulation. This result indicates that hierarchical clustering is a useful tool for detecting fine patterns during exploration of biological data, and that it can play an important role in comparative studies.

scholarly journals Large scale hierarchical clustering of protein sequences

2007 ◽  
Author(s):  
Antje Krause ◽  
Jens Stoye ◽  
Martin Vingron
Keyword(s):  
Hierarchical Clustering ◽  
Large Scale ◽  
Protein Sequences

scholarly journals Profiles of Galaxy Clusters in Cosmological Scenarios

1987 ◽  
Vol 117 ◽  
pp. 287-287
Author(s):  
Michael J. West ◽  
Avishai Dekel ◽  
Augustus Oemler
Keyword(s):  
High Resolution ◽  
Galaxy Clusters ◽  
Hierarchical Clustering ◽  
Large Scale ◽  
Power Spectra ◽  
Clusters Of Galaxies ◽  
Spatial Symmetry ◽  
Abell Clusters ◽  
Wide Range ◽  
Large Scale Simulations

We have studied the properties of rich clusters of galaxies in various cosmological scenarios by comparing high resolution N-body simulations with observations of Abell clusters. The clusters have been simulated in two steps. First, protoclusters are identified in large-scale simulations which represent a wide range of cosmological scenarios (hierarchical clustering, pancake scenarios, and hybrids of the two, spanning a range of power spectra). Then the region around each protocluster is simulated with high resolution, the particles representing L* galaxies. The protoclusters have no spatial symmetry built into them initially. The final clusters are still dynamically young, and of moderate densities, which should be representative of Abell clusters of richness classes 1 and 2.

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