Abstract
In view of the intrinsic drawbacks of traditional clustering methods, e.g. the sensitivity to initialization and the risk of falling into local optima, we introduce two new tools to enhance clustering performance via Swarm Intelligence (SI), i.e. Self-Aggregation (SA) and Eccentricity Analysis (EA), which are based on Firefly Algorithm (FA) in this paper. In order to confirm the effectiveness of the techniques, an improved k-means++ method is given as an instance. Large experiments illustrate that our algorithm performs better on both accuracy and robustness than the existing ones.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderberg, M.R.: Cluster analysis for applications (No. OAS-TR-73-9). Office of the Assistant for Study Support Kirtland Afb N Mex (1973)
Ketchen, D.J., Shook, C.L.: The application of cluster analysis in strategic management research: An analysis and critique. Strategic Management Journal 17(6), 441–458 (1996)
Punj, G., Stewart, D.W.: Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research, 134–148 (1983)
Abshouri, A.A., Bakhtiary, A.: A New Clustering Method Based on Firefly and KHM. Journal of Communication and Computer 9(4), 387–391 (2012)
Aljarah, I., Ludwig, S.A.: A new clustering approach based on glowworm swarm optimization. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2642–2649. IEEE (2013)
Hassanzadeh, T., Meybodi, M.R.: A new hybrid approach for data clustering using firefly algorithm and K-means. In: 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 007-011. IEEE (2012)
Huang, Z., Zhou, Y.: Using glowworm swarm optimization algorithm for clustering analysis. Journal of Convergence Information Technology 6(2), 78–85 (2011)
Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firefly algorithm: Performance study. Swarm and Evolutionary Computation 1(3), 164–171 (2011)
Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)
Yang, X.S.: Nature-inspired metaheuristic algorithms. Luniver Press (2010)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: The Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1(281-297), p. 14 (1967)
Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: The Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)
Krishnanand, K.N.: Glowworm swarm optimization: A multimodal function optimization paradigm with applications to multiple signal source localization tasks (2009)
Krishnanand, K.N., Ghose, D.: Glowworm swarm optimisation: A new method for optimising multi-modal functions. International Journal of Computational Intelligence Studies 1(1), 93–119 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Gu, J., Wen, K. (2014). Self-aggregation and Eccentricity Analysis: New Tools to Enhance Clustering Performance via Swarm Intelligence. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-11857-4_52
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11856-7
Online ISBN: 978-3-319-11857-4
eBook Packages: Computer ScienceComputer Science (R0)