Pseudo-hierarchical ant-based clustering using a heterogeneous agent hierarchy and automatic boundary formation

Brown, Jeremy B.; Huber, Manfred

doi:10.1145/1830483.1830499

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Performance is judged by how quickly the algorithms converged to the correct number of clusters built into the data, and how long its took for the DB measure to converge to a stable number. More detailed and complete results can be found in [10].…”

Section: Datamentioning

confidence: 99%

“…In all cases, PHAC showed significantly better scaling behavior, further underlining the benefits of the extensions presented here. Figure 14 shows the scaling characteristics in terms of the number of data items (see [10] for additional data). These graphs show that while both algorithms behave relatively similar for smaller datasets, PHAC scales significantly better to larger item numbers ant thus more complex clustering tasks.…”

Section: Scalabilitymentioning

confidence: 99%

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Pseudo-Hierarchical Ant-Based Clustering

Brown

Huber

2010

2010 IEEE International Conference on Systems, Man and Cybernetics

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The behavior and self-organization of ant colonies has been widely studied to address distributed clustering.However, most models that directly mimic ants produce too many clusters and converge too slowly. A wide range of research has attempted to address this through various means, but a number of sources of inefficiency remain, including: i) ants must physically move from one cluster to another through intermediate locations, ii) patterns in movement among clusters is not considered, and iii) while some approaches have included bulk item movement, they do not provide efficient movement while still maintaining the self-organizing nature of ant-based clustering. To address these issues, this paper presents a new algorithm for ant-based clustering. Here ants maintain a movement zone around each cluster, keeping ants close to data items. These movement zones are used to elect representatives that are responsible for all long distance movement.Representatives can, probabilistically, pass an object it has to any other representative. Since each cluster has approximately one representative at any given time, the search space for placing items over a long distance is reduce to the number of clusters.This provides an infrastructure that allows bulk movement and efficient long distance merging.

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Section: Datamentioning

confidence: 99%

Section: Scalabilitymentioning

confidence: 99%

Pseudo-Hierarchical Ant-Based Clustering

Brown

Huber

2010

2010 IEEE International Conference on Systems, Man and Cybernetics

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ACC-FMD: ant colony clustering for functional module detection in protein-protein interaction networks

Liu

Zhang

et al. 2015

IJDMB

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Mining functional modules in Protein-Protein Interaction (PPI) networks is a very important research for revealing the structure-functionality relationships in biological processes. More recently, some swarm intelligence algorithms have been successfully applied in the field. This paper presents a new nature-inspired approach, ACC-FMD, which is based on ant colony clustering to detect functional modules. First, some proteins with the higher clustering coefficients are, respectively, selected as ant seed nodes. And then, the picking and dropping operations based on ant probabilistic models are developed and employed to assign proteins into the corresponding clusters represented by seeds. Finally, the best clustering result in each generation is used to perform the information transmission by updating the similarly function. Experimental results on some benchmarked datasets show that ACC-FMD outperforms the CFinder and MCODE algorithms and has comparative performance with the MINE, COACH, DPClus and Core algorithms in terms of the general evaluation metrics.

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Pseudo-hierarchical ant-based clustering using a heterogeneous agent hierarchy and automatic boundary formation

Cited by 2 publications

References 8 publications

Pseudo-Hierarchical Ant-Based Clustering

Pseudo-Hierarchical Ant-Based Clustering

ACC-FMD: ant colony clustering for functional module detection in protein-protein interaction networks

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