Abstract
We present in this paper a new hybrid algorithm for data clustering. This algorithm discovers automatically clusters in numerical data without prior knowledge of a possible number of classes, without any initial partition, and without complex parameter settings. It uses the stochastic and exploratory principles of an ant colony with the deterministic and heuristic principles of the K-means algorithm. Ants move on a 2D board and may load or drop objects. Dropping an object on an existing heap of objects depends on the similarity between this object and the heap. The K-means algorithm improves the convergence of the ant colony clustering. We repeat two stochastic/deterministic steps and introduce hierarchical clustering on heaps of objects and not just objects. We also use other refinements such as an heterogeneous population of ants to avoid complex parameters settings, and a local memory in each ant. We have applied this algorithm on standard databases and we get very good results compared to the K-means and ISODATA algorithms.
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References
Ball G.H. and Hall D.J., ISODATA, a novel method of data analysis and pattern classification, Technical report, Stanford Research Institute, 1965.
Deneubourg J.-L., Goss S., Franks N., Sendova-Pranks A., Detrain C. and Chretien L. (1990), The dynamic of collective sorting robot-like ants and ant-like robots, Proceedings of the first Conference on Simulation of Adaptive Behavior 1990, J.A. Meyer and S.W. Wilson (Eds), MIT Press/Bradford Books, pp 356–363.
Fresneau D. (1985), Individual foraging and path fidelity in a ponerine ant, Insectes Sociaux, Paris, 1985, Volume 32, n 2, pp 109–116.
Goss S. and Deneubourg J.-L. (1991), Harvesting by a group of robots, Proceedings of the first European Conference on Artificial Life 1991, F.J. Varela and P. Bourgine (Eds), MIT press/Bradford Books, pp 195–204.
Jain A.K and Dubes R.C. (1988), Algorithms for Clustering Data, Prentice Hall Advanced Reference Series.
Kuntz P. et Snyers D. (1994), Emergent colonization and graph partitioning, Proceedings of the third International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3 (SAB94), D. Cliff, P. Husbands, J.A. Meyer, S.W. Wilson (Eds), MIT-Press, pp 494–500.
Kuntz P., Layzell P. and Snyers D. (1997), A colony of Ant-like agents for partitioning in VLSI technology, Proceedings of the fourth European Conference on Artificial Life 1994, P. Husbands and I. Harvey (Eds), MIT press, pp 417–424.
Lumer E.D. and Faieta B.(1994), Diversity and Adaptation in Populations of Clustering Ants, Simulation of Adaptive Behavior: From Animals to Animats 3 (SAB94), D. Cliff, P. Husbands, J.A. Meyer, S.W. Wilson (Eds), MIT-Press, pp 501–508.
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© 1999 Springer-Verlag Berlin Heidelberg
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Monmarché, N., Slimane, M., Venturini, G. (1999). On Improving Clustering in Numerical Databases with Artificial Ants. In: Floreano, D., Nicoud, JD., Mondada, F. (eds) Advances in Artificial Life. ECAL 1999. Lecture Notes in Computer Science(), vol 1674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48304-7_83
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DOI: https://doi.org/10.1007/3-540-48304-7_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66452-9
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