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This section presents the performance evaluation of the graph clustering-based approaches that are splitting approach (GraphS) and merging approach (GraphM).
Available data from all surveys were analyzed using a thematic content analysis approach; graphs were used to provide visual representations of the information.
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Research in assembly planning can be categorised into three types of approach: graph-based, knowledge-based and artificial intelligence approaches.
This paper addresses several types of potential conflicts and consistency properties with a systematic and rigorous approach: graph theory, network flow technology and colored Petri nets are applied for specifying and verifying a secure interoperation design.
Following this approach, graph metrics and the GMV of nodes were all normalised (z-scored) on a within-subject level.
In our approach, graph transition energy is defined to quantify the similarity between collections of images.
To address the limitations of the existing methods, we propose a novel approach, Graph-regularized Dual Lasso (GDL), which simultaneously learns the association between SNPs and genes and refines the prior networks.
Normally, various social computing related techniques include statistical approaches, graph based approaches and so forth.
The metabolic pathfinding problem itself can be further divided into two different approaches: graph-based pathfinding and constraint-based pathfinding.
Graph Based Approaches: Graph theory related techniques like minimal spanning tree and max-flow min-cut etc. are used for clustering.
Experimental results show that the proposed algorithms (GM-SMCC and EGM-SMCC) can lead to performance superior to other compared feature-based approaches, graph-based approaches, and collective classification algorithms.
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