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References
MacQueen J. B. (1967) Some methods for classification and analysis of multivariate observations, In: Fifth Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, CA, Vol. 1, pp. 281-297
Deneubourg J. L., Goss S., Franks N., Sendova-Franks A., Detrain C., Chretien L. (1991) The dynamics of collective sorting: robot-like ant and ant-like robot. In: First Conf. on Simulation of Adaptive Behavior: from animals to animats, Cambridge, MA: MIT Press, pp. 356-365
Lumer E., Faieta B. (1994) Diversity and adaptation in populations of clustering ants. In: Third Int. Conf. on Simulation of Adaptive Behavior: from animals to animats 3, Cambridge, MA: MIT Press, pp. 499-508
Bonabeau E., Dorigo M., Theraulaz G. (1999) Swarm intelligence: From natural to artificial system. Oxford University Press, New York
Handl J., Knowles J., Dorigo M. (2004) Strategies for the increased robustness of ant-based clustering. In: Engineering Self-Organising Systems, LNCS 2977, SpringerVerlag, Heidelberg, pp. 90-104
Dempster, A. P., Laird, N. M., Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistical Society, Series B, 39(1):1-38
Handl J., Meyer B. (2002) Improved ant-based clustering and sorting in a document retrieval interface. In: Seventh International Conference on Parallel Problem Solving from Nature, LNCS 2439, Springer-Verlag, Berlin, Germany, pp. 913-923
. Yang Y., Kamel M. (2003) Clustering ensemble using swarm intelligence. In: IEEE Swarm Intelligence Symposium 2003, pp. 65-71
Hoe K. M., Lai W. K., Tai S. Y. (2002) Homogeneous ants for web document similarity modeling and categorization. In: Third Int. Workshop on Ant Algorithms (ANTS2002), Brussels, Belgium, LNCS 2463, Springer-Verlag, Berlin, Heidelberg, Germany, pp. 256-261
Ramos V., Abraham A. (2003) Swarms on continuous data. In: Fifth Congress on Evolutionary Computation (CEC2003), Canberra, Australia, IEEE Press, pp. 1370-1375
. Chialvo D. R., Millonas M. M. (1995) How swarms build cognitive maps. In: Luc Steels (ed), The Biology and Technology of Intelligent Autonomous Agents, 144, NATO ASI Series, pp. 439-450
Abraham A., Ramos V. (2003) Web using mining using artificial ant colony clustering and linear genetic programming, In: Fifth Congress on Evolutionary Computation (CEC2003), Canberra, Australia, IEEE Press, pp. 1384-1391
Tsang W., Kwong S. (2005) Unsupervised anomaly intrusion detection using ant colony clustering model. In: Fourth IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST2005), Muroran, Japan, pp. 223-232
Ramos V., Abraham A. (2005) ANTIDS: Self-organized ant-based clustering model for intrusion detection system. In: Fourth IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST2005), Muroran, Japan, pp. 977-986
Banerjee S., Grosan C., Abraham A., Mahanti P. K. (2005) Intrusion detection on sensor networks using emotional ants. Int. J. of Applied Science and Computations, USA
. Albuquerque P., Dupuis A. (2002) A parallel cellular ant colony algorithm for clustering and sorting. In: S. Bandini, B. Chopard and M. Tomassini (eds.), In: Fifth Int. Conf. on Cellular Automata for Research and Industry, (ACRI2002), LNCS 2493. Springer, pp. 220-230
. Monmarch é N., Slimane M., Venturini G. (1999) AntClass: discovery of clusters in numeric data by an hybridization of an ant colony with the Kmeans algorithm. Technical Report 213, Laboratoire d’Informatique, E3i, University of Tours
. Mikami T., Wada M. (2001) Data visualization method for growing self-organizing networks with ant clustering algorithm. In: Sixth European Conference on Artificial Life (ECAL2001), Prague, Czech Republic, LNAI 2159, pp. 623-626
. Kanade P. M., Hall L. O. (2003) Fuzzy Ants as a Clustering Concept. In: 22nd Int. Conf. of the North American Fuzzy Information Processing Society, pp. 227-232
. Schockaert S., De Cock M., Cornelis C., Kerre E. E. (2004) Efficient clustering with fuzzy ants. In: Applied Computational Intelligence, World Scientific Press, pp. 195-200
Shannon C. E., Weaver W. (1949) The Mathematical Theory of Communication. The University of Illinois Press, Urbana, IL
Theraulaz G., Gautrais J., Camazine S., Deneubourg J.-L. (2003) The formation of spatial patterns in social insects: from simple behaviours to complex structures. Philosophical Transactions of the Royal Society of London A, 361, pp. 1263-1282
Parunak H. V. D., Brueckner S., Sauter J. A., Posdamer J. (2001) Mechanics and military applications for synthetic pheromones. In: 2001 Workshop on Autonomy Oriented Computation, Montreal, Canada
. UCI Machine Learning Repository [Online]. Available: http://www.ics.uci.edu/mlearn/MLRepository.html
Boley D. (1998) Principle direction divisive partitioning. J. Data Mining and Knowledge Discovery, Vol. 2, Dec 1998, pp. 325-344
Strehl A., Ghosh J., Mooney R. (2000) Impact of similarity measures on web-page clustering. In: 7th National Conf. on Artificial Intelligence: Workshop of Artificial Intelligence for Web Search, AAAI, Austin, Texas, USA, pp. 58-64
Dunn J. C. (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernetics, Vol. 3, 1973, pp. 32-57
Bell A. J., Sejnowski T. J. (1995) An information-maximization approach to blind separation and blind deconvolution. J. Neural Computation, Vol. 7, Nov. 1995, pp.1129-1159
Lee T.-W., Girolami M., Sejnowski T. J. (1999) Independent component analysis using an extended informax algorithm for mixed sub-gaussian and super-gaussian sources. J. Neural Computation, Vol. 11, Feb. 1999, pp. 417-441
Hyv ärinen A. (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks, Vol. 10, May. 1999, pp. 626-634
. Elkan C. (2000) Results of the KDD’99 classifier learning. In: ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Boston, MA, 1(2): 63-64
. Maheshkumar S., Gursel S. (2003) Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context. In: Int. Conf. on Machine Learning, Models, Technologies and Applications, Las Vegas, Nevadat, USA, CSREA Press, pp. 209-215
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Tsang, CH., Kwong, S. (2006). Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection. In: Abraham, A., Grosan, C., Ramos, V. (eds) Swarm Intelligence in Data Mining. Studies in Computational Intelligence, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-34956-3_5
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