On data clustering with a flock of artificial agents

Picarougne, Fabien; Azzag, Hanene; Venturini, Gilles; Guinot, Christiane

doi:10.1109/ictai.2004.95

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…In addition to the ant-based clustering algorithms, some other studies that have used swarm-based techniques for the clustering purpose have been introduced in [2,22,23]. Folino et al in [2,22] introduced swarm-based clustering techniques based on adaptive flocking algorithms.…”

Section: Related Work: Bio-inspired Agent-based Clustering Algorithmsmentioning

confidence: 99%

“…SPARROW combines the flocking algorithm with the density based DBSCAN algorithm [22] and SPARROW-SNN combines the flocking algorithm with a shared nearest neighbor (SNN) clustering algorithm [2] to discover clusters with differing sizes, shapes, and densities in noisy, high dimensional data sets. Picarougne et al [23] also introduced a clustering algorithm using concepts of a flock of agents that moves together in a complex manner with simple local rules. In this method, each agent represents one data item and agents move to create homogeneous groups evolving together in a 2D environment.…”

Section: Related Work: Bio-inspired Agent-based Clustering Algorithmsmentioning

confidence: 99%

“…In many studies [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21], ant-like agents have been used to solve the clustering problem and the resulted algorithms constitute a remarkable part of the existing bioinspired clustering algorithms. Also, some other studies using flocks or swarms of mobile agents have been introduced in [2,22,23] for the clustering purpose. These algorithms are called swarm-based in the following parts of the paper.…”

Section: Introductionmentioning

confidence: 99%

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An agent-based clustering algorithm using potential fields

Baghshah

Shouraki

Lucas

2008

2008 IEEE/ACS International Conference on Computer Systems and Applications

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In this paper, a novel clustering algorithm using an agent-based architecture along with a force-based clustering algorithm is proposed. To this end, a set of simple mobile agents that have limited processing power is used. These agents communicate in a pairwise manner to exchange their position information. As opposed to the bio-inspired clustering algorithms that need a set of local rules to specify the agent movements, in this paper the agent motions are driven from attractive and repulsive potential fields that are created by the data points and the other agents respectively. Each agent moves according to the resulted force from applying the potential fields and announces its next position to the other agents. The movement of agents is continued until all of them reach equilibrium points.978-1-4244-1968-5/08/$25.00 ©2008 IEEE

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Section: Related Work: Bio-inspired Agent-based Clustering Algorithmsmentioning

confidence: 99%

Section: Related Work: Bio-inspired Agent-based Clustering Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An agent-based clustering algorithm using potential fields

Baghshah

Shouraki

Lucas

2008

2008 IEEE/ACS International Conference on Computer Systems and Applications

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“…More recently, an approach based on flocks of agents, known as FClust, was used for data clustering [129,128,117,116]. The flock-based approach holds a great promise, and being the most recent of all the swarm-based clustering approaches, it has been the least studied.…”

Section: Flocks Of Agents-based Clusteringmentioning

confidence: 99%

“…Recently, flocks of agents [116] and Ant Colony Optimization (ACO) [41,73] methods have also been used for clustering. In the flock of agents approach, each agent represents one data item.…”

Section: Searchingmentioning

confidence: 99%

Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

Saka¹

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Improvements in Flock-Based Collaborative Clustering Algorithms

Saka¹,

Nasraoui²

2009

Intelligent Systems Reference Library

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Abstract.Inspiration from nature has driven many creative solutions to challenging real life problems. Many optimization methods, in particular clustering algorithms, have been inspired by such natural phenomena as neural systems and networks, natural evolution, the immune system, and lately swarms and colonies. In this paper, we make a brief survey of swarm intelligence clustering algorithms and focus on the flocks of agents-based clustering and data visualization algorithm, (FClust). A few limitations of FClust are then discussed with proposed improvements. We thus propose the FClust-annealing algorithm that decreases the number of iterations needed to converge and improves the quality of resulting clusters. We also propose a (Kmeans+FClust) hybrid algorithm which decreases the complexity of FClust from quadratic to linear, with further improvements in the cluster quality. Experiments on both artificial and real data illustrate the workings of FClust and the advantages of our proposed variants.

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On data clustering with a flock of artificial agents

Cited by 5 publications

References 3 publications

An agent-based clustering algorithm using potential fields

An agent-based clustering algorithm using potential fields

Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

Improvements in Flock-Based Collaborative Clustering Algorithms

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