Abstract
Semi-supervised clustering can take advantage of some labeled data called seeds to bring a great benefit to the clustering of unlabeled data. This paper uses the seeding-based semi-supervised idea for a fuzzy clustering method inspired by diffusion processes, which has been presented recently. To investigate the effectiveness of our approach, experiments are done on three UCI real data sets. Experimental results show that the proposed algorithm can improve the clustering performance significantly compared to other semi-supervised clustering approaches.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fillippone, M., Camastra, F., Masulli, F., Rovetta, S.: A survey of kernel and spectral methods for clustering. Pattern Recognition 41(1), 176–190 (2008)
Jain, A.K., Murty, M.N., Flyn, P.J.: Data clustering: a review. ACM Computing Surveys 32(3), 256–323 (1999)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Tou, J.T., Gonzalez, R.C.: Pattern recognition principles. Addison-Wesley, London (1974)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)
Kim, D.W., Lee, K.Y., Lee, D., Lee, K.H.: A kernel-based subtractive clustering method. Pattern Recognition Letters 26(7), 879–891 (2005)
Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: Neural-gas network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4(4), 558–569 (1993)
Ormella, C., Anastasios, M., Sandhya, S., Don, K., Sijia, L., Philip, K.M., Radek, E.: DifFUZZY: a fuzzy clustering algorithm for complex datasets. International Journal of Computational Intelligence in Bioinformatics and Systems Biology 1(4), 402–417 (2010)
Basu, S., Banerjee, A., Mooney, R.J.: Semi-supervised clustering by seeding. In: Proceedings of the Nineteenth International Conference on Machine Learning, pp. 27–34 (2002)
Grira, N., Crucianu, M., Boujemaa, N.: Active semi-supervised fuzzy clustering. Pattern Recognition 41(5), 1834–1844 (2008)
Basu, S., Banjeree, A., Mooney, R.J.: Active semi-supervised for pairwise constrained clustering. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 333–344 (2004)
UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLSummary.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gu, L. (2013). The Novel Seeding-Based Semi-supervised Fuzzy Clustering Algorithm Inspired by Diffusion Processes. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_68
Download citation
DOI: https://doi.org/10.1007/978-3-642-39065-4_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
eBook Packages: Computer ScienceComputer Science (R0)