Identifying multiple influential spreaders by a heuristic clustering algorithm

2017 ◽  
Vol 381 (11) ◽  
pp. 976-983 ◽  
Author(s):  
Zhong-Kui Bao ◽  
Jian-Guo Liu ◽  
Hai-Feng Zhang
Keyword(s):  
Clustering Algorithm ◽  
Heuristic Clustering

scholarly journals Assembly of an Interactive Correlation Network for the Arabidopsis Genome Using a Novel Heuristic Clustering Algorithm

2009 ◽  
Vol 152 (1) ◽  
pp. 29-43 ◽  
Author(s):  
Marek Mutwil ◽  
Björn Usadel ◽  
Moritz Schütte ◽  
Ann Loraine ◽  
Oliver Ebenhöh ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Correlation Network ◽  
Arabidopsis Genome ◽  
Heuristic Clustering

scholarly journals Assessing and Improving Methods Used in Operational Taxonomic Unit-Based Approaches for 16S rRNA Gene Sequence Analysis

2011 ◽  
Vol 77 (10) ◽  
pp. 3219-3226 ◽  
Author(s):  
Patrick D. Schloss ◽  
Sarah L. Westcott
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Operational Taxonomic Unit ◽  
Rrna Gene ◽  
Operational Taxonomic Units ◽  
Gene Sequence Analysis ◽  
Technical Advances ◽  
Heuristic Clustering

ABSTRACTIn spite of technical advances that have provided increases in orders of magnitude in sequencing coverage, microbial ecologists still grapple with how to interpret the genetic diversity represented by the 16S rRNA gene. Two widely used approaches put sequences into bins based on either their similarity to reference sequences (i.e., phylotyping) or their similarity to other sequences in the community (i.e., operational taxonomic units [OTUs]). In the present study, we investigate three issues related to the interpretation and implementation of OTU-based methods. First, we confirm the conventional wisdom that it is impossible to create an accurate distance-based threshold for defining taxonomic levels and instead advocate for a consensus-based method of classifying OTUs. Second, using a taxonomic-independent approach, we show that the average neighbor clustering algorithm produces more robust OTUs than other hierarchical and heuristic clustering algorithms. Third, we demonstrate several steps to reduce the computational burden of forming OTUs without sacrificing the robustness of the OTU assignment. Finally, by blending these solutions, we propose a new heuristic that has a minimal effect on the robustness of OTUs and significantly reduces the necessary time and memory requirements. The ability to quickly and accurately assign sequences to OTUs and then obtain taxonomic information for those OTUs will greatly improve OTU-based analyses and overcome many of the challenges encountered with phylotype-based methods.

Service Requirements Distribution Algorithms in Carpooling Problem

2013 ◽  
Vol 409-410 ◽  
pp. 1296-1302
Author(s):  
Shu Xia Dong ◽  
Zeng Zhen Shao
Keyword(s):  
Heuristic Algorithm ◽  
Traffic Congestion ◽  
Clustering Algorithm ◽  
Utilization Rate ◽  
Vehicle Simulation ◽  
Total Vehicle ◽  
Matching Degree ◽  
Simulation Results ◽  
Distribution Algorithms ◽  
Heuristic Clustering

The traffic congestion problem and logistics costs are becoming more and more serious. There is no doubt that carpooling may not only improve vehicle utilization rate but also reduce logistics costs. In multi-vehicle environment, how to distribute service requirements to a proper vehicle is to be solved firstly. In this paper, a heuristic clustering algorithm is introduced to solve grouping progress in deterministic carpooling problem. Based on characteristics of service requirements and vehicles, the conception matching degree and heuristic algorithm are proposed in order to distribute requirements into one specific vehicle. Simulation results show that the algorithm can improve the success ride rate greatly and reduce total vehicle costs.

HC_AB: A new heuristic clustering algorithm based on Approximate Backbone

2011 ◽  
Vol 111 (17) ◽  
pp. 857-863 ◽  
Author(s):  
Yu Zong ◽  
Guandong Xu ◽  
Ping Jin ◽  
Yanchun Zhang ◽  
Enhong Chen
Keyword(s):  
Clustering Algorithm ◽  
Heuristic Clustering
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