Ant clustering with locally weighted ant perception and diversified memory
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Ant clustering algorithms are a robust and flexible tool for clustering data that have produced some promising results. This paper introduces two improvements that can be incorporated into any ant clustering algorithm: kernel function similarity weights and a similarity memory model replacement scheme. A kernel function weights objects within an ant’s neighborhood according to the object distance and provides an alternate interpretation of the similarity of objects in an ant’s neighborhood. Ants can hill-climb the kernel gradients as they look for a suitable place to drop a carried object. The similarity memory model equips ants with a small memory consisting of a sampling of the current clustering space. We test several kernel functions and memory replacement schemes on the Iris, Wisconsin Breast Cancer, and Lincoln Lab network intrusion datasets. Compared to a basic ant clustering algorithm, we show that kernel functions and the similarity memory model increase clustering speed and cluster quality, especially for datasets with an unbalanced class distribution, such as network intrusion.
KeywordsAnt clustering Locally weighted regression Intrusion detection system
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- Cheeseman, P., & Stutz, J. (1996). Bayesian classification (autoclass): theory and results. In Advances in knowledge discovery and data mining (pp. 153–180). Menlo Park, CA: AAAI Press. Google Scholar
- Cucchiara, R. (1993). Analysis and comparison of different genetic models for the clustering problem in image analysis. In International conference on artificial neural networks and genetic algorithms (pp. 423–427). Berlin: Springer. Google Scholar
- Dasgupta, D., & Gonzales, F. (2002). An immunity-based technique to characterize intrusions in computer networks. IEEE Transactions on Evolutionary Computation, 6, 179–188. Google Scholar
- Deneubourg, J.-L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., & Chrétien, L. (1990). The dynamics of collective sorting robot-like ants and ant-like robots. In Proceedings of the first international conference on simulation of adaptive behavior: from animals to animats (pp. 356–363). Cambridge, MA: MIT Press. Google Scholar
- Ekola, T., Laurikkala, M., Lehto, T., & Koivisto, H. (2004). Network traffic analysis using clustering ants. In Proceedings of the 17th world automation congress (pp. 275–280). Los Alamitos, CA: IEEE Computer Society Press. Google Scholar
- Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annual Eugenics, 7(II), 179–188. Google Scholar
- Fisher, D. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2(2), 139–172. Google Scholar
- Haines, J., Lippmann, R., Fried, D., Tran, E., Boswell, S., & Zissman, M. (1999). DARPA intrusion detection system evaluation: Design and procedures (Technical report). MIT Lincoln Laboratory Technical Report. Google Scholar
- Hamerly, G., & Elkan, C. (2003). Learning the k in k-means. In Advances in neural information processing systems (pp. 281–288). Cambridge, MA: MIT Press. Google Scholar
- Handl, J. (2003). Ant-based methods for tasks of clustering and topographic mapping: Extensions, analysis and comparison with alternative methods (Master’s thesis). Germany: Universität Erlangen-Nürnberg. URL http://www.handl.julia.de.
- Handl, J., Knowles, J., & Dorigo, M. (2003a). Ant-based clustering: a comparative study of its relative performance with respect to k-means, average link, and 1d-som (Technical report TR/IRIDIA/2003-24). Université Libre de Bruxelles. Google Scholar
- Handl, J., Knowles, J., & Dorigo, M. (2003b). On the performance of ant-based clustering. In Frontiers in artificial intelligence and applications : Vol. 104. Design and application of hybrid intelligent systems (pp. 204–213). Amsterdam: IOS Press. Google Scholar
- Jones, D. R., & Beltrano, M. A. (1991). Solving partitioning problems with genetic algorithms. In Fourth international conference on genetic algorithms (pp. 442–449). San Mateo, CA: Morgan Kaufmann. Google Scholar
- Kanade, P. M., & Hall, L. O. (2004). Fuzzy ant clustering by centroid positioning. In Proceedings of the 2004 IEEE international conference on fuzzy systems (pp. 371–376). Los Alamitos, CA: IEEE Computer Society Press. Google Scholar
- Luc̆ić, P. (2002). Modelling transportation systems using concepts of swarm intelligence and soft computing (Ph.D. thesis). Virginia Polytechnic Institute. Google Scholar
- Lumer, E. B., & Faieta, B. (1994). Diversity and adaptation in populations of clustering ants. In Third international conference on simulation of adaptive behavior: From animals to animats (pp. 501–508). Cambridge, MA: MIT Press. Google Scholar
- MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5-th Berkeley symposium on mathematical statistics and probability (pp. 281–297). Berkeley, CA: University of California Press. Google Scholar
- Mangasarian, O. L., & Wolberg, W. H. (1990). Cancer diagnosis via linear programming. SIAM News, 23(5), 1–18. Google Scholar
- Monmarché, N. (1999). On data clustering with artificial ants. In A. A. Freitas (Ed.), Data mining with evolutionary algorithms: Research directions—AAAI-99 and GECCO-99 workshop (pp. 23–26). Menlo Park, CA: AAAI Press. Google Scholar
- Montes de Oca, M. A., Garrido, L., & Aguirre, J. L. (2004). A first approach to study the effects of direct information exchange between agents in ant-based clustering. In S. Kumar, A. Abraham, J. Harnisch, & A. Satyadas (Eds.), Proceedings of the first world congress on lateral computing WCLC 2004. World Federation on Lateral-Computing, Bangalore, India. Google Scholar
- Newman, D. J., Hettich, S., Blake, C. L., & Merz, C. J. (1998). UCI repository of machine learning databases. URL http://www.ics.uci.edu/~mlearn/MLRepository.html.
- Pomerlau, D. (1993). Knowledge-based training of artificial neural networks for autonomous robot driving. In J. H. Connel, & S. Mahadevan (Eds.), Robot Learning. Dordrecht: Kluwer Academic. Google Scholar
- Schockaert, S., De Cock, M., Cornelis, C., & Kerre, E. E. (2004b). Fuzzy ant based clustering. In LNCS : Vol. 3172. Ant colony optimization and swarm intelligence (pp. 342–349). Berlin: Springer. Google Scholar