Abstract
Many algorithms perform data clustering by compressing the original data into a more compact and interpretable representation, which can be more easily inspected for the presence of clusters. This, however, can be a risky alternative, because the simplified representation may contain distortions mainly related to the density information present in the data, which can considerably act on the clustering results. In order to treat this deficiency, this paper proposes an Adaptive Radius Immune Algorithm (ARIA), which is capable of maximally preserving the density information after compression by implementing an antibody adaptive suppression radius that varies inversely with the local density in the space. ARIA is tested with both artificial and real world problems obtaining a better performance than the aiNet algorithm and showing that preserving the density information leads to refined clustering results.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bezerra, G.B., de Castro, L.N.: Bioinformatics data analysis using an artificial immune network. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 22–33. Springer, Heidelberg (2003)
de Castro, L.N., Von Zuben, F.J.: aiNet: An artificial Immune Network for Data Analysis. In: Abbass, H.A., Saker, R.A., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, ch. XII, pp. 231–259. Idea Group Publishing, USA (2001)
De Stefano, C., D’Elia, C., Marcelly, A.: A Dynamic Approach to Learning Quantization. In: Proc. Of the 17th International Conference on Pattern Recognition, vol. 4, pp. 601–604 (2004)
Eisen, M.B., Spellman, P.T., Brow, P.O., Botstein, D.: Cluster Analysis and Display of Genome-wide Expression Patterns. Proc. Natl. Acad. Sci., USA 95, 14863–14868 (1998)
Everitt, B., Landau, S., Leese, M.: Cluster Analysis, 4th edn. Oxford University Press, Oxford (2001)
Knidel, H., de Castro, L.N., Von Zuben, F.J.: Data Clustering with a Neuro-immune Network. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 1279–1288. Springer, Heidelberg (2005)
Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences. Springer, Heidelberg (2001)
Silverman, B.W.: Density estimation for statistics and data analysis. Champan and Hall, London (1986)
Timmis, J., Neal, M.: A resource Limited Artificial Immune System for Data Analysis. Knowledge Based Systems 14(3-4), 121–130 (2001)
Ultsch, A.: U*-Matrix: a Tool to Visualize Clusters in High Dimensional Data, in Tech-nical Report No. 36, Department of Mathematics and Computer Science Philipps-University Marburg (2003)
Zahn, C.T.: Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters. IEEE Trans. on Computers C-20(1), 68–86 (1971)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bezerra, G.B., Barra, T.V., de Castro, L.N., Von Zuben, F.J. (2005). Adaptive Radius Immune Algorithm for Data Clustering. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds) Artificial Immune Systems. ICARIS 2005. Lecture Notes in Computer Science, vol 3627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536444_22
Download citation
DOI: https://doi.org/10.1007/11536444_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28175-7
Online ISBN: 978-3-540-31875-0
eBook Packages: Computer ScienceComputer Science (R0)