Greedy Search Approach of Graph Mining

2012 ◽  
Keyword(s):  
Graph Mining ◽  
Greedy Search ◽  
Search Approach

Greedy Search Approach of Graph Mining

2011 ◽  
pp. 483-489
Author(s):  
Xinhua Zhang ◽  
Novi Quadrianto ◽  
Kristian Kersting ◽  
Zhao Xu ◽  
Yaakov Engel ◽  
...  
Keyword(s):  
Graph Mining ◽  
Greedy Search ◽  
Search Approach

Investigation of Environmental Monitoring Designs for Corporate Management Information Systems

2010 ◽  
pp. 138-154 ◽  
Author(s):  
Marina G. Erechtchoukova ◽  
Stephen Y. Chen ◽  
Peter A. Khaiter
Keyword(s):  
Environmental Management ◽  
Information System ◽  
Management Information System ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Corporate Management ◽  
Greedy Search ◽  
Management Information ◽  
Corporate Environmental Management ◽  
Search Approach

The evaluation of an organization’s environmental performance is an integral part of a corporate environmental management information system. This chapter considers an organization’s environmental impact assessment with respect to a water resource. It investigates formal approaches to the development of temporal monitoring designs for producing data sufficient to perform the assessment. In this study, simple random sampling, stratified random sampling, and designs obtained using greedy search have been investigated with respect to their compatibility with a corporate environmental management information system. All three approaches determine temporal monitoring designs with minimal costs and supply data sufficient for estimation of water quality indicators for a given level of uncertainty. It is shown that monitoring designs obtained using the greedy search approach will outperform other designs when the level of uncertainty in the estimate must be low. If high levels of uncertainty are tolerable, simple random designs become preferable due to their simplicity and effectiveness. The proposed approaches lead to automated procedures which can be easily integrated into a corporate environmental management information system.

scholarly journals Inferring structure in bipartite networks using the latent blockmodel and exact ICL

2017 ◽  
Vol 5 (1) ◽  
pp. 45-69 ◽  
Author(s):  
JASON WYSE ◽  
NIAL FRIEL ◽  
PIERRE LATOUCHE
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Network Data ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Greedy Search ◽  
Number Of Clusters ◽  
Model Based Clustering ◽  
Search Approach

AbstractWe consider the task of simultaneous clustering of the two node sets involved in a bipartite network. The approach we adopt is based on use of the exact integrated complete likelihood for the latent blockmodel. Using this allows one to infer the number of clusters as well as cluster memberships using a greedy search. This gives a model-based clustering of the node sets. Experiments on simulated bipartite network data show that the greedy search approach is vastly more scalable than competing Markov chain Monte Carlo-based methods. Application to a number of real observed bipartite networks demonstrate the algorithms discussed.

Sign in / Sign up

Export Citation Format

Share Document