## An aggregated clustering approach using multi-ant colonies algorithms.(English)Zbl 1095.68106

Summary: This paper presents a multi-ant colonies approach for clustering data that consists of some parallel and independent ant colonies and a queen ant agent. Each ant colony process takes different types of ants moving speed and different versions of the probability conversion function to generate various clustering results with an ant-based clustering algorithm. These results are sent to the queen ant agent and combined by a hypergraph model to calculate a new similarity matrix. The new similarity matrix is returned back to each ant colony process to re-cluster the data using the new information. Experimental evaluation shows that the average performance of the aggregated multi-ant colonies algorithms outperforms that of the single ant-based clustering algorithm and the popular $$K$$-means algorithm. The result also shows that the lowest outliers strategy for selecting the current data set has the best performance quality.

### MSC:

 68T10 Pattern recognition, speech recognition 68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) 68W05 Nonnumerical algorithms

### Software:

AntNet; UCI-ml; HAS-QAP
Full Text:

### References:

 [1] Bonabeau, E.; Dorigo, M.; Theraulaz, G., Swarm intelligence—from natural to artificial system, (1999), Oxford University Press New York · Zbl 1003.68123 [2] Dorigo, M.; Gambardella, L.M., Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE trans. evolut. comput., 1, 53-66, (1997) [3] Gambardella, L.M.; Taillard, E.D.; Dorigo, M., Ant colonies for the quadratic assignment problem, J. oper. res. soc., 50, 167-176, (1999) · Zbl 1054.90621 [4] Maniezzo, V.; Colrni, A., The ant system applied to the quadratic assignment problem, IEEE trans. knowledge data eng., 11, 769-778, (1999) [5] Colrni, A.; Dorigo, M.; Maniezzo, V.; Trubian, M., Ant system for job shop scheduling, Belgian J. oper. res., 34, 39-53, (1994) · Zbl 0814.90047 [6] Caro, G.D.; Dorigo, M., Antnet: distributed stigmergic control for communication networks, J. artif. intell. res., 9, 317-355, (1998) · Zbl 0910.68182 [7] Cube, C.R.; Zhang, H., Task modeling in collective robotics, Auton robots, 4, 53-72, (1997) [8] Deneubourg, J.L.; Goss, S.; Franks, N.; Sendova-Franks, A.; Detrain, C.; Chretien, L., The dynamics of collective sorting: robot-like ant and ant-like robot, (), 356-365 [9] E. Lumer, B. Faieta, Diversity and adaptation in populations of clustering ants, in: Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animals, vol. 3, MIT Press, Cambridge, MA, 1994, pp.499-508. [10] B. Wu, Z. Shi, A clustering algorithm based on swarm intelligence, in: Proceedings of the IEEE International Conferences on Info-tech & Info-net, Beijing, China, 2001, pp.58-66. [11] J. Handl, B. Meyer, Improved ant-based clustering and sorting in a document retrieval interface. in: J.J.M. Guervos et al., (eds.), Parallel Problem Solving from Nature—PPSN VII, Seventh International Conference, Granada, Spain, 2002 [Online]. Available: http://wwwcip.informatik.uni-erlangen.de/ sijuhand/HandlMeyerPPSN2002.pdf. [12] V. Ramos, J.J. Merelo, Self-organized stigmergic document maps: environment as a mechanism for context learning, in: E. Alba, F. Herrera, J.J. Merelo et al. (Eds.), AEB’2002—First Spanish Conference on Evolutionary and Bio-Inspired Algorithms, Centro Univ. de Mérida, Mérida, Spain, February 2002, pp. 284-293. [13] N. Monmarché, M. Slimane, G. Venturini, Antclass: discovery of clusters in numeric data by a hybridization of an ant colony with the $$k$$-means algorithm. Internal Report No. 213, Laboratoire d’Informatique de l’Université de Tours, E3i Tours, 1999 [Online]. Available: http://www.antsearch.univ-tours.fr/publi/MonSliVen99b.pdf [14] B. Wu, Y. Zheng, S. Liu, Z. Shi, CSIM: a document clustering algorithm based on swarm intelligence, IEEE World Congress on Computational Intelligence, Hawaiian, 2002, pp. 477-482. [15] Y. Yang, M. Kamel, Clustering ensemble using swarm intelligence, IEEE Swarm Intelligence Symposium, Indianapolis, USA, April, 2003, pp. 65-71. [16] Michels, R.; Middendorf, M., An ant system for the shortest common supersequence problem, (), 51-61 [17] Middendorf, M.; Reischle, F.; Schmeck, H., Multi colony ant algorithms, J. heuristic, 8, 305-320, (2002) · Zbl 1012.68792 [18] E. Talbi, O. Roux, C. Fonlupt, D. Robillard, Parallel ant colonies for combinatorial optimization problems, in: J. Rolim et al. (Eds.), Parallel and Distributed Processing, vol. 11, IPPS/SPDP’99 Workshops, Lecture Notes in Computer Science, vol. 1586, Springer, Berlin, 1999, pp. 239-247. [19] Kawamura, H.; Yamamoto, M., Multiple ant colonies algorithm based on colony level interactions, IEICE trans. fund., E83-A, 2, 371-379, (2000) [20] A. Strehl, J. Ghosh, Cluster ensembles—a knowledge reuse framework for combining partitionings. in: Proceedings of the Conference on Artificial Intelligence (AAAI 2002), AAAI/MIT Press, July 2002, pp. 93-98. [21] H. Ayad, M. Kamel, Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors, in: Fourth International Workshop on Multiple Classifier Systems (MCS 2003), Lecture Notes in Computer Science, vol. 2709, Springer, Berlin, 2003. · Zbl 1040.68586 [22] P.M. Murpy, D.W. Aha, UCI repository of machine learning databases. University of California, Irvine, CA, 1994. [Online]. Available: http://www.ics.uci.edu/mlearn/MLRepository.html. [23] [Online]. Available: http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html. [24] Y. Yang, M. Kamel, F. Jin, Topic discovery from document using ant-based clustering combination, in: Web Technologies Research and Development—APWeb 2005, 7th Asia-Pacific Web Conference, Shanghai, China, Lecture Notes in Computer Science, vol. 3399, Springer, UK, 2005, pp. 100-108. [25] Theodoridis, S.; Koutroubas, K., Pattern recognition, (1999), Academic Press New York [26] Halkidi, M.; Batistakis, Y.; Vazirgiannis, M., On clustering validation techniques, J. intell. inform. syst., 17, 2-3, 107-145, (2001) · Zbl 0998.68154 [27] He, J.; Tan, A.; Tan, C.; Sung, S., On quantitative evaluation of clustering systems, () [28] M. Halkidi, M. Vazirgiannis, Y. Batistakis, Quality scheme assessment in the clustering process, in: Proceedings of the Fourth European Conference Principles and Practice of Knowledge Discovery in Databases (PKDD), 2000, pp.165-276. · Zbl 0998.68154
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