• Corpus ID: 41490204

A New Clustering Boids Algorithm for Data Mining

@inproceedings{David2009ANC,
  title={A New Clustering Boids Algorithm for Data Mining},
  author={M. David and Leandro Nunes de Castro},
  year={2009}
}
This paper presents a multi-agent flocking approach for data clustering. The new algorithm, called cBoids, is proposed based on the classic Boids model with a few changes on the Boids behavior. In this new algorithm, each Boid represents an object from the database and the original three rules from the Boids model were changed so that the values in the database have influence on their behavior and two other rules were added. The new rules are responsible for the creation and destruction of… 

Figures from this paper

A Clustering Method Using Simplified Swarm Intelligence Algorithm

This paper presents a Bird flocking algorithm that uses the concepts of a flock of agents moving together in a complex manner with simple local rules to create homogeneous groups of data in a 2D environment.

Comparing the Clustering Efficiency of ACO and K-Harmonic Means Techniques

Empirical results clearly show that ant based clustering algorithm performs well compared to another technique called K-Harmonic means clusteringgorithm, which is particularly suitable to perform exploratory data analysis.

Image segmentation by intelligent clustering technique

  • Subarna SinhaS. Deb
  • Computer Science
    2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS)
  • 2013
This paper used the Bird flocking algorithm that uses the concepts of a flock of agents to perform image segmentation to solve the task of segmentation of images which optimize the partition of image data into homogenous regions.

Association Rule Mining for Web Recommendation

The association rule mining algorithms for better Web Recommendation and Web Personalization are proposed for better web recommendation and web personalization.

References

SHOWING 1-8 OF 8 REFERENCES

An Adaptive Flocking Algorithm for Spatial Clustering

A parallel spatial clustering algorithm based on the use of new Swarm Intelligence techniques that combines a smart exploratory strategy based on a flock of birds with a density-based cluster algorithm to discover clusters of arbitrary shape and size in spatial data.

A Distributed Agent Implementation of Multiple Species Flocking Model for Document Partitioning Clustering

This paper proposes a bio-inspired clustering model, the Multiple Species Flocking clusteringmodel (MSF), and presents a distributed multi-agent MSF approach for document clustering.

Dynamically Adaptive Data Clustering Using Intelligent Swarm-like Agents

A novel clustering method, the PSDC, a new Particle Swarm-like agents approach for Dynamically Adaptive data clustering, which does not require initial partitioned seeds and it can dynamically adapt to the changes in the global shape or size of the clusters.

Data clustering: a review

An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.

An evolutionary immune network for data clustering

A novel immune network model is proposed with the main goals of clustering and filtering unlabelled numerical data sets, and a trade-off between the proposed network and artificial neural networks used to perform unsupervised learning is concluded.

Flocks, herds, and schools: a distributed behavioral model

This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually, an elaboration of a particle system, with the simulated birds being the particles.

THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS

Ant-based and swarm-based clustering

There are two main types of ant-based clustering: the first group of methods directly mimics the clustering behavior observed in real ant colonies, and the second group is less directly inspired by nature and is reformulated as an optimization task.