Abstract
Semi-supervised clustering ensemble has emerged as an important elaboration of classical clustering problem that improves quality and robustness in clustering by combining the results of different clustering components with user provided constraints. In this paper, we propose a novel semi-supervised consensus clustering algorithm based on multi-ant colonies. Our method incorporates pairwise constraints not only in each ant colony clustering process, but also in computing new similarity matrix during the multi-ant colonies ensemble. Experimental results demonstrate the effectiveness of the proposed method.
This work is partially supported by the National Science Foundation of China (Nos. 61170111, 61003142 and 61152001) and the Fundamental Research Funds for the Central Universities (No. SWJTU11ZT08).
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
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann (2006)
Topchy, A., Jain, A.K., Punch, W.: Clustering ensembles: models of consensus and weak partitions. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(12), 1866–1881 (2005)
Strehl, A., Ghosh, J.: Cluster ensembles–a knowledge reuse framework for combining multiple partitions. Machine Learning Research 3, 583–617 (2002)
Yang, Y., Kamel, M.S.: An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recognition 39(7), 1278–1289 (2006)
Ayad, H.G., Kamel, M.S.: On voting-based consensus of cluster ensembles. Pattern Recognition 43(5), 1943–1953 (2010)
Jia, J.H., Xiao, X., Liu, B.X., et al.: Bagging-Based Spectral Clustering Ensemble Selection. Pattern Recognition Letters 32(10), 1456–1467 (2011)
Masson, M.H., Denoeux, T.: Ensemble clustering in the belief functions framework. International Journal of Approximate Reasoning 52(1), 92–109 (2011)
Duan, C., Cleland-Huang, J., Mobasher, B.: A consensus based approach to constrained clustering of software requirements. In: Proceeding of the 17th ACM Conference on Information and Knowledge Management (CIKM 2008), pp. 1073–1082 (2008)
Greene, D., Cunningham, P.: Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 140–151. Springer, Heidelberg (2007)
Iqbal, A.M., Moh’d, A., Khan, Z.A.: Semi-supervised clustering ensemble by voting. In: Proceeding of the International Conference on Information and Communication Systems (ICICS 2009), pp. 1–5 (2009)
Wang, H.J., et al.: Semi-Supervised Cluster Ensemble Model Based on Bayesian Network. Journal of Software 21(11), 2814–2825 (2010)
Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: Proceeding of the International Conference on Machine Learning, pp. 1103–1110 (2000)
Yang, Y., Kamel, M.: Clustering ensemble using swarm intelligence. In: Proceeding of IEEE Swarm Intelligence Symposium, pp. 65–71 (2003)
Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Proceeding of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats, vol. 3, pp. 499–508 (1994)
Yang, Y., Chen, J., Tan, W.: Enhancing ant-based clustering using pairwise constraints. In: Proceeding of the 4th International Conference on Intelligent Systems & Knowledge Engineering (ISKE 2009), pp. 76–81 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, Y., Wang, H., Lin, C., Zhang, J. (2012). Semi-supervised Clustering Ensemble Based on Multi-ant Colonies Algorithm. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-31900-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31899-3
Online ISBN: 978-3-642-31900-6
eBook Packages: Computer ScienceComputer Science (R0)