Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 235790, 10 pages
http://dx.doi.org/10.1155/2015/235790
Research Article

A Partitioning Based Algorithm to Fuzzy Tricluster

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China

Received 17 October 2014; Revised 30 December 2014; Accepted 1 January 2015

Academic Editor: Zhan Shu

Copyright © 2015 Yongli Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. Liu, Y. Ouyang, and Z. Xiong, “Incremental clustering using information bottleneck theory,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 25, no. 5, pp. 695–712, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. K. M. Hammouda and M. S. Kamel, “Efficient phrase-based document indexing for web document clustering,” IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 10, pp. 1279–1296, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. M. A. Rahman and M. Z. Islam, “A hybrid clustering technique combining a novel genetic algorithm with K-means,” Knowledge-Based Systems, vol. 71, pp. 345–365, 2014. View at Publisher · View at Google Scholar
  4. E. Rashedi, A. Mirzaei, and M. Rahmati, “An information theoretic approach to hierarchical clustering combination,” Neurocomputing, vol. 148, pp. 487–497, 2015. View at Publisher · View at Google Scholar
  5. S. Wikaisuksakul, “A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering,” Applied Soft Computing, vol. 24, pp. 679–691, 2014. View at Publisher · View at Google Scholar
  6. Y. Liu, Q. Guo, L. Yang, and Y. Li, “Research on incremental clustering,” in Proceedings of the 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet '12), pp. 2803–2806, Yichang, China, April 2012. View at Publisher · View at Google Scholar
  7. K. Honda, C.-H. Oh, Y. Matsumoto, A. Notsu, and H. Ichihashi, “Exclusive partition in FCM-type co-clustering and its application to collaborative filtering,” International Journal of Computer Science and Network Security, vol. 12, no. 12, pp. 52–58, 2012. View at Google Scholar
  8. C.-H. Oh, K. Honda, and H. Ichihashi, “Fuzzy clustering for categorical multivariate data,” in Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, vol. 4, pp. 2154–2159, July 2001. View at Publisher · View at Google Scholar
  9. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY, USA, 1981. View at MathSciNet
  10. N. Gupta and S. Aggarwal, “MIB: using mutual information for biclustering gene expression data,” Pattern Recognition, vol. 43, no. 8, pp. 2692–2697, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Hanczar and M. Nadif, “Ensemble methods for biclustering tasks,” Pattern Recognition, vol. 45, no. 11, pp. 3938–3949, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Han and H. Yan, “BSN: an automatic generation algorithm of social network data,” Journal of Systems and Software, vol. 84, no. 8, pp. 1261–1269, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. W.-C. Tjhi and L. Chen, “A partitioning based algorithm to fuzzy co-cluster documents and words,” Pattern Recognition Letters, vol. 27, no. 3, pp. 151–159, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Hanmandlu, O. P. Verma, S. Susan, and V. K. Madasu, “Color segmentation by fuzzy co-clustering of chrominance color features,” Neurocomputing, vol. 120, pp. 235–249, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. W.-C. Tjhi and L. Chen, “Possibilistic fuzzy co-clustering of large document collections,” Pattern Recognition, vol. 40, no. 12, pp. 3452–3466, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. W.-C. Tjhi and L. Chen, “Robust fuzzy co-clustering algorithm,” in Proceedings of the 6th International Conference on Information, Communications and Signal Processing (ICICS '07), pp. 1–5, December 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Yan, L. Chen, and W.-C. Tjhi, “Fuzzy semi-supervised co-clustering for text documents,” Fuzzy Sets and Systems, vol. 215, pp. 74–89, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. D. Gnatyshak, D. I. Ignatov, A. Semenov, and J. Poelmans, “Analysing online social network data with biclustering and triclustering,” in Proceedings of the 2nd International Workshop on Concept Discovery in Unstructured Data (CDUD '12), pp. 30–39, May 2012. View at Scopus
  19. P. S. Mahiskar, A. W. Bhade, and P. N. Chatur, “An effective triclustering algorithm for mining real datasets: review paper,” International Journal Computer Technology & Applications, vol. 3, no. 1, pp. 352–355, 2012. View at Google Scholar
  20. R. Guigourès, M. Boullé, and F. Rossi, “A triclustering approach for time evolving graphs,” in Proceedings of the IEEE 12th International Conference on Data Mining Workshops (ICDMW '12), pp. 115–122, Brussels, Belgium, December 2012. View at Publisher · View at Google Scholar
  21. MovieLens, http://grouplens.org/datasets/movielens/.