Abstract
This paper describes a new artificial immune system algorithm for data clustering. The proposed algorithm resembles the CLONALG, widely used AIS algorithm but much simpler as it uses one shot learning and omits cloning. The algorithm is tested using four simulated and two benchmark data sets for data clustering. Experimental results indicate it produced the correct clusters for the data sets.
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References
de Castro, L.N., Von Zuben, F.J.: The Clonal Selection Algorithm with Engineering Applications (full version, pre-print). In: Proceedings of GECCO 2000, Workshop on Artificial Immune Systems and Their Applications, pp. 36–37 (2000)
de Castro, L.N., Von Zuben, F.J.: aiNet: An Artificial Immune Network for Data Analysis (Full version, pre-print). In: Abbass, H.A., Sarker, R.A., Newton, C.S. (eds.) Book Chapter in Data Mining: A Heuristic Approach, Idea Group Publishing, USA (2001)
de Castro, L.N., Timmis, J.: Artificial Immune Systems: A Novel Paradigm to Pattern Recognition(pre-print). In: Corchado, J.M., Alonso, L., Fyfe, C. (eds.) Artificial Neural Networks in Pattern Recognition, SOCO 2002, pp. 67–84. University of Paisley, UK (2002)
de Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems 6(3), 239–251 (2002)
Jerne, N.K.: Towards a network theory of the immune system. Ann, Immunol (Inst. Pasteur) 125C, 373–389 (1974)
Nasaroui, O., Gonzales, F., Dasgupta, D.: The fuzzy artificial immune system: Motivations, basic concepts, and application to clustering and Web profiling. In: Proc. of the IEEE International Conf. On Fuzzy Systems at WCCI, Hawaii, May 12-17, pp. 711–716 (2002)
Perelson, A.: Immune network theory. immunological review 110, 5–36 (1989)
Timmis, J.: Artificial immune systems: A novel data analysis technique inspired by the immune network theory, Ph. D thesis, Department of Computer Science, University of Wales, Aberystwyth, Ceredigion, Wales (2000)
Wierzchon, S., Kuzelewska, U.: Stable Clusters Formation in an Artificial Immune System. In: Timmis, J., Bentley, P.J. (eds.) Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS), University of Kent at Canterbury, University of Kent at Canterbury Printing Unit (2002), pp. 68–75 (2002)
Fahlman, S.E., Lebiere, C.: The Cascade-Correlation Learning Architecture. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems, 2, pp. 524–532. Morgan Kaufmann, San Mateo (1990)
Ultsch, A.: Self-Organizing Neural Networks Perform Different from Statistical k-means, Gesellschaft für Klassifica-tion (1995)
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Younsi, R., Wang, W. (2004). A New Artificial Immune System Algorithm for Clustering. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_9
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DOI: https://doi.org/10.1007/978-3-540-28651-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
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