Abstract
This paper presents a new algorithm for clustering a large amount of data. We improved the ant colony clustering algorithm that uses an ant’s swarm intelligence, and tried to overcome the weakness of the classical cluster analysis methods. In our proposed algorithm, improvements in the efficiency of an agent operation were achieved, and a new function “cluster condensation” was added. Our proposed algorithm is a processing method by which a cluster size is reduced by uniting similar objects and incorporating them into the cluster condensation. Compared with classical cluster analysis methods, the number of steps required to complete the clustering can be suppressed to 1% or less by performing this procedure, and the dispersion of the result can also be reduced. Moreover, our clustering algorithm has the advantage of being possible even in a small-field cluster condensation. In addition, the number of objects that exist in the field decreases because the cluster condenses; therefore, it becomes possible to add an object to a space that has become empty. In other words, first, the majority of data is put on standby. They are then clustered, gradually adding parts of the standby data to the clustering data. The method can be adopted for a large amount of data. Numerical experiments confirmed that our proposed algorithm can theoretically applied to an unrestricted volume of data.
Similar content being viewed by others
References
Bonabeau, E., Dorigo, M., Theraulaz, G., 1999. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, USA.
Lumer, E.D., Faieta, B., 1994. Diversity and Adaptation in Populations of Clustering Ants. Proceedings of the 3rd International Conference on the Simulation of Adaptive Behavior, p.501–508.
Shohdohji, T., Samura, N., Yano, F., Toyoda, Y., 2007. An Improvement of Ant Colony Clustering Algorithm Based on Ant Behavior. Proceedings of the 37th International Conference on Computers and Industrial Engineering, p.13–21.
Author information
Authors and Affiliations
Corresponding author
Additional information
Project (No. 18510132) supported by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research
Rights and permissions
About this article
Cite this article
Shohdohji, T., Yano, F. & Toyoda, Y. A new algorithm based on metaheuristics for data clustering. J. Zhejiang Univ. Sci. A 11, 921–926 (2010). https://doi.org/10.1631/jzus.A1001030
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.A1001030