인터넷정보학회논문지 (Journal of Internet Computing and Services)

  • 제15권5호
  • /
  • Pages.95-105
  • /
  • 2014
  • /
  • 1598-0170(pISSN)
  • /
  • 2287-1136(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
권호 표지
DOI QR코드

DOI QR Code

개미 군락 시스템을 이용한 계층적 클러스터 분석

Ant Colony Hierarchical Cluster Analysis

  • 투고 : 2014.04.21
  • 심사 : 2014.09.15
  • 발행 : 2014.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 방향그래프에서 개미가 한 노드에서 다른 노드들로 이동하는 새로운 개미 기반계층적 클러스터링 알고리즘을 제안한다. 노드페로몬은 로컬영역에서 상대 밀도값으로 간주될 수 있는 값으로 노드로 들어오는 에지들의 페로몬 양을 합한 것이다. 일정한 횟수만큼 개미들을 이동시킨 후 방향 그래프로부터 소량의 노드페로몬 값을 가진 노드들을 제거하고, 강하게 연결되어 있는 요소들을 하나의 클러스터로 구성한다. 반복적으로 낮은 값부터 높은 값까지 제거작업을 하여 계층적 클러스터들을 구축한다. 다양한 실험을 통해 제안하는 알고리즘과 기존 클러스터링 알고리즘을 비교하고 제안하는 알고리즘의 우수성을 실험을 통해 입증한다.

In this paper, we present a novel ant-based hierarchical clustering algorithm, where ants repeatedly hop from one node to another over a weighted directed graph of k-nearest neighborhood obtained from a given dataset. We introduce a notion of node pheromone, which is the summation of amount of pheromone on incoming arcs to a node. The node pheromone can be regarded as a relative density measure in a local region. After a finite number of ants' hopping, we remove nodes with a small amount of node pheromone from the directed graph, and obtain a group of strongly connected components as clusters. We iteratively do this removing process from a low value of threshold to a high value, yielding a hierarchy of clusters. We demonstrate the performance of the proposed algorithm with synthetic and real data sets, comparing with traditional clustering methods. Experimental results show the superiority of the proposed method to the traditional methods.

키워드

참고문헌

  1. Rui Xu and Donald Wunsch II, "Survey of Clustering Algorithms," IEEE Transactions On Neural Networks, Vol. 16, No. 3, pp. 645-678, May 2005. https://doi.org/10.1109/TNN.2005.845141
  2. Yan Yang and M. S. Kamel, "An aggregated clustering approach using multi-ant colonies algorithms," Pattern Recognition Vol. 39, Issue 7, pp. 1278-1289, July 2006. https://doi.org/10.1016/j.patcog.2006.02.012
  3. Deneubourg, J.-L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., and Chretien, L., "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 1, 1991, pp. 356-365, Cambridge, MA: MIT Press.
  4. Lumer, E. and Faieta, B., "Diversity and adaptation in populations of clustering ants," in Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3, pp. 501-508, Cambridge, MA: MIT Press.
  5. Handl, J. and Meyer, B., "Improved ant-based clustering and sorting in a document retrieval interface," in Proceedings of the Seventh International Conference on Parallel Problem Solving from Nature, 2002, Vol. 2439 of Lecture Notes in Computer Science, pp.913-923. Berlin, Germany: Springer-Verlag.
  6. Handl, J., Knowles, J., and Dorigo M., "Ant-based clustering and topographic mapping," Artificial Life, Vol. 12, No. 1, pp. 35-62, 2006. https://doi.org/10.1162/106454606775186400
  7. Shang Liu, Zhi-Tong Dou, Fei Li, Ya-Lou Huang, "A New Ant Colony Clustering Algorithm Based on DBSCAN," in Machine Learning and Cybernetics, Proc. of 3th International Conf., 2004, vol. 3, pp 1491-1496.
  8. N. Monmarche, 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, France, Jan. 1999.
  9. Ling Chen, Li Tu, Hong-Jian Chen, "Data Clustering by Ant Colony on a Digraph," in Machine Learning and Cybernetics, Proc. of 4th International Conf., Guangzhou Aug. 18-21, 2005, vol. 3, pp 1686-1692.
  10. Gunjan Gupta, Alexander Liu, Joydeep Ghosh, "Hierarchical Density Shaving: A clustering and visualization framework for large biological datasets," in Proc. 6th IEEE International Conf. on Data, Washington, 2006, pp 89-93.
  11. Ramos, V. and Merelo, J., "Self-organized stigmergic document maps: Environment as a mechanism for context learning," in Proceedings of the First Spanish Conference on Evolutionary and Bio-Inspired Algorithms, Spain, 2002, pp. 284-293.
  12. Kuntz, P and Snyers, D., "Emergent colonization and graph partitioning," in Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3, 1994, pp. 494-500, Cambridge, MA: MIT Press.
  13. Kuntz, P and Snyers, D., "New results on an ant-based heuristic for highlighting the organization of large graphs," in Proceedings of the Congress of Evolutionary Computation, 1999, pp. 1451-1458, IEEE Press.
  14. Gupta G, Liu A, Ghosh J., "Automated hierarchical density shaving: a robust automated clustering and visualization framework for large biological data sets.",IEEE/ACM Trans Comput Biol Bioinform. 2010 Apr-Jun;7(2):223-237 https://doi.org/10.1109/TCBB.2008.32
  15. KiYoung Lee, Dae-Won Kim, K. H. Lee, and D. Lee, "Density-Induced Support Vector Data Description," IEEE Transactions on Neural Networks, Vol. 18, No. 1, pp. 284-289, January 2007. https://doi.org/10.1109/TNN.2006.884673
  16. Marco Dorigo and Thomas Stutzle, Ant Colony Optimization, MIT Press, 2004.
  17. Soumen Chakrabarti, mining the Web: Discovering Knowledge from Hypertext Data, Morgan Karufmann Publishers, 2003.
  18. Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, John Wiley & Sons, 2001.
  19. Blake, C. and Merz, C. UCI repository of machine learning databases, Technical report, Department of Information and Computer Sciences, University of California, Irvine.
  20. M. Halkidi, M. Vazirgiannis, and I. Batistakis, "Quality scheme assessment in the clustering process," in Proceedings of the Fourth European Conference on Principles of Data Mining and Knowledge Discovery, Vol. 1910 of Lecture Notes in Computer Science, pp. 265-267, 2000.
  21. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2006.
  22. Debajit Sensarma, Koushik Majumder, "A Novel Hierarchical Ant based QoS aware Intelligent Routing Scheme for MANETS", International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.6, November 2013
  23. Hanene Azzag, Gilles Venturini, Antoine Oliver and Christiane Guinot, "A hierarchical ant based clustering algorithm and its use in three real-world applications", European Journal of Operational Research, Vol. 179, Issue 3, 16 June 2007, pp. 906-922 https://doi.org/10.1016/j.ejor.2005.03.062
  24. J. Chircop and C. D Buckingham, "A Multiple Pheromone Ant Clustering Algorithm", Proceedings of NICSO 2013, to be published in Studies in Computational Intelligence, Springer, 2013
  25. Jan Chircop, Christopher D. Buckingham, "The Multiple Pheromone Ant Clustering Algorithm and its application to real world domains", Proceedings of the 2013 Federated Conference on Computer Science and Information Systems pp. 27-34
  26. Kumar V and Balasubramanie P, "Ant Colony Optimization Using Hierarchical Clustering in Mobile Ad Hoc Networks", European Journal of Scientific Research, 2011, Vol. 61 Issue 4, p549-560.
  27. O.A. Mohamed Jafar and R. Sivakumar, "Ant-based Clustering Algorithms: A Brief Survey" ,International Journal of Computer Theory and Engineering, Vol. 2, No. 5, October, 2010, 1793-8201
  28. Wafa'a Omar, Amr Badr and Abd El-Fattah Hegazy, "Hybrid Ant-Based Clustering Algorithm With Cluster Analysis Techniques", Journal of Computer Science 9(6): 780-793, 2013 https://doi.org/10.3844/jcssp.2013.780.793