scholarly journals Clustering Methods for Hierarchical Traffic Grooming in Large-Scale Mesh WDM Networks

2010 ◽  
Vol 2 (8) ◽  
pp. 502 ◽  
Author(s):  
Bensong Chen ◽  
George N. Rouskas ◽  
Rudra Dutta
Keyword(s):  
Large Scale ◽  
Traffic Grooming ◽  
Wdm Networks ◽  
Clustering Methods

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

Traffic grooming for survivable WDM networks-shared protection

2003 ◽  
Vol 21 (9) ◽  
pp. 1367-1383 ◽  
Author(s):  
Canhui Ou ◽  
Keyao Zhu ◽  
Hui Zang ◽  
L.H. Sahasrabuddhe ◽  
B. Mukherjee
Keyword(s):  
Traffic Grooming ◽  
Wdm Networks ◽  
Shared Protection

EdClust: A heuristic sequence clustering method with higher sensitivity

2021 ◽  
Author(s):  
Ming Cao ◽  
Qinke Peng ◽  
Ze-Gang Wei ◽  
Fei Liu ◽  
Yi-Fan Hou
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Method ◽  
Cluster Number ◽  
Sequence Clustering ◽  
Downstream Analysis ◽  
Heuristic Clustering

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

Dynamic Traffic Grooming under a Differentiated Resilience Scheme for WDM Mesh Networks

2011 ◽  
pp. 181-194
Author(s):  
Taisir E.H. El-Gorashi ◽  
Jaafar Elmirghani
Keyword(s):  
Optical Fibre ◽  
Level Scheme ◽  
Mesh Networks ◽  
Traffic Grooming ◽  
Wdm Networks ◽  
Bandwidth Efficiency ◽  
Dynamic Traffic ◽  
Wavelength Division ◽  
Telecommunications Services ◽  
Dynamic Traffic Grooming

Due to its huge bandwidth, optical fibre is currently widely deployed to provide a variety of telecommunications services and applications. Wavelength-division multiplexing (WDM) has emerged as the technology of choice to harness the huge bandwidth available in an optical fibre. Traffic grooming supports efficient utilization of network resources by allowing sub-wavelength granularity connections to be groomed onto a single lightpath. Fault-tolerance for WDM networks is a major architectural and design issue as a single link failure can cause loss of an enormous amount of information. However, providing 100% guaranteed resilience to all types of traffic supported by existing and future networks may be unnecessary and wasteful in terms of resource utilization and cost efficiency. This chapter investigates the problem of dynamic traffic grooming for WDM networks under a differentiated resilience scheme. We propose two differentiated resilience schemes at different grooming levels— Differentiated Resilience at Lightpath (DRAL) level scheme, and Differentiated Resilience at Connection (DRAC) level scheme. These schemes explore different ways of provisioning backup paths and tradeoff between bandwidth efficiency and the number of required grooming ports. Both schemes support three resilience classes: dedicated protection, shared protection, and restoration. Simulation is carried out to evaluate and compare the two differentiated resilience schemes. Simulation results show that the DRAL scheme is not very sensitive to the changes in the number of grooming ports, while the DRAC scheme utilizes grooming ports more aggressively as it trades grooming ports for bandwidth efficiency in routing and grooming.

scholarly journals Early alterations of large-scale brain networks temporal dynamics in young children with autism

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Aurélie Bochet ◽  
Holger Franz Sperdin ◽  
Tonia Anahi Rihs ◽  
Nada Kojovic ◽  
Martina Franchini ◽  
...  
Keyword(s):  
Large Scale ◽  
Developmental Stages ◽  
Temporal Dynamics ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Brain Network ◽  
Clustering Methods ◽  
Early Developmental Stages ◽  
Spatio Temporal ◽  
Early Developmental

AbstractAutism spectrum disorders (ASD) are associated with disruption of large-scale brain network. Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD. Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 113 toddlers and preschoolers (66 with ASD) using an EEG microstate approach. We first determined the predominant microstates using established clustering methods. We identified five predominant microstate (labeled as microstate classes A–E) with significant differences in the temporal dynamics of microstate class B between the groups in terms of increased appearance and prolonged duration. Using Markov chains, we found differences in the dynamic syntax between several maps in toddlers and preschoolers with ASD compared to their TD peers. Finally, exploratory analysis of brain–behavioral relationships within the ASD group suggested that the temporal dynamics of some maps were related to conditions comorbid to ASD during early developmental stages.

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