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The Scientific World Journal
Volume 2014, Article ID 870406, 7 pages
http://dx.doi.org/10.1155/2014/870406
Research Article

Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data

1Department of Process Engineering, University of Pannonia, Veszprém 8200, Hungary
2Bioinformatics & Scientific Computing Core, Campus Science Support Facilities, Vienna Biocenter, 1030 Vienna, Austria

Received 15 August 2013; Accepted 4 December 2013; Published 30 January 2014

Academic Editors: Y. Blanco Fernandez and Y.-B. Yuan

Copyright © 2014 András Király 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. R. Agrawal, T. Imieliński, and A. Swami, “Mining association rules between sets of items in large databases,” in Proceedings of the ACM SIGMOD Record, vol. 22, pp. 207–216, ACM, 1993.
  2. “Fimi'03: workshop on frequent itemset mining implementations,” in Proceedings of the IEEE International Conference on Data Mining Workshop on Frequent Itemset Mining Implementations, B. Göthals and M. J. Zaki, Eds., Melbourne, Fla, USA, December 2003.
  3. “Fimi'04: workshop on frequent itemset mining implementations,” in Proceedings of the IEEE International Conference on Data Mining Workshop on Frequent Item set Mining Implementations, R. Bayardo, B. Göthals, and M. J. Zaki, Eds., Brighton, UK, 2004.
  4. S. C. Madeira and A. L. Oliveira, “Biclustering algorithms for biological data analysis: a survey,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 1, no. 1, pp. 24–45, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Busygin, O. Prokopyev, and P. M. Pardalos, “Biclustering in data mining,” Computers & Operations Research, vol. 35, no. 9, pp. 2964–2987, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Tanay, R. Sharan, and R. Shamir, “Discovering statistically significant biclusters in gene expression data,” Bioinformatics, vol. 18, supplement 1, pp. S136–S144, 2002. View at Google Scholar · View at Scopus
  7. A. Prelić, S. Bleuler, P. Zimmermann et al., “A systematic comparison and evaluation of biclustering methods for gene expression data,” Bioinformatics, vol. 22, no. 9, pp. 1122–1129, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Pearson Addison Wesley, Boston, Mass, USA, 2006.
  9. R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proceedings of the 20th International Conference on Very Large Data Bases (VLDB '94), vol. 1215, pp. 487–499, Santiago, Chile, September 1994.
  10. H. Mannila, H. Toivonen, and A. I. Verkamo, “Efficient algorithms for discovering association rules,” in Proceedings of the AAAI Workshop on Knowledge Discovery in Databases (KDD '94), pp. 181–192, Seattle, Wash, USA, 1994.
  11. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo, “Fast discovery of association rules,” in Advances in Knowledge Discovery and Data Mining, vol. 12, pp. 307–328, American Association for Artificial Intelligence, Menlo Park, Calif, USA, 1996. View at Google Scholar
  12. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, “Discovering frequent closed itemsets for association rules,” in Database Theory—ICDT’99, Lecture Notes in Computer Science, pp. 398–416, Springer, London, UK, 1999. View at Publisher · View at Google Scholar
  13. J. Pei, J. Han, and R. Mao, “CLOSET: an efficient algorithm for mining frequent closed itemsets,” in Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, vol. 4, pp. 21–30, Dallas, Tex, USA, 2000.
  14. J. M. Zaki and C.-J. Hsiao, “CHARM: an efficient algorithm for closed association rule mining,” in Proceedings of the 2nd SIAM International Conference on Data Mining, pp. 457–473, 1999.
  15. G. Grahne and J. Zhu, “Efficiently using prefix-trees in mining frequent itemsets,” in Proceedings of the Workshop on Frequent Itemset Mining Implementations (FIMI '03), pp. 123–132, 2003. View at Google Scholar
  16. G. Liu, H. Lu, W. Lou, and J. X. Yu, “On computing, storing and querying frequent patterns,” in Proceeding of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '03), pp. 607–612, Washington, DC, USA, August 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Wang, J. Han, and J. Pei, “CLOSET+: searching for the best strategies for mining frequent closed itemsets,” in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '03), pp. 236–245, Washington, DC, USA, August 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Vo, T.-P. Hong, and B. Le, “DBV-miner: a dynamic bit-vector approach for fast mining frequent closed itemsets,” Expert Systems with Applications, vol. 39, no. 8, pp. 7196–7206, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Y. Rodriguez-Gonzalez, J. F. Martinez-Trinidad, J. A. Carrasco-Ochoa, and J. Ruiz-Shulcloper, “Mining frequent patterns and association rules using similarities,” Expert Systems with Applications, vol. 40, no. 17, pp. 6823–6836, 2013. View at Publisher · View at Google Scholar
  20. J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions,” Data Mining and Knowledge Discovery, vol. 15, no. 1, pp. 55–86, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. J. S. Park, M. S. Chen, and P. S. Yu, An Effective Hash-Based Algorithm for Mining Association Rules, vol. 24, ACM, 1995.
  22. A. Amir, R. Feldman, and R. Kashi, “A new and versatile method for association generation,” Information Systems, vol. 22, no. 6-7, pp. 333–347, 1997. View at Google Scholar · View at Scopus
  23. R. J. Bayardo, “Efficiently mining long patterns from databases,” in Proceedings of the ACM Sigmod Record, vol. 27, pp. 85–93, ACM, 1998.
  24. F. P. Pach, A. Gyenesei, and J. Abonyi, “Compact fuzzy association rule-based classifier,” Expert Systems with Applications, vol. 34, no. 4, pp. 2406–2416, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Gyenesei, U. Wagner, S. Barkow-Oesterreicher, E. Stolte, and R. Schlapbach, “Mining co-regulated gene profiles for the detection of functional associations in gene expression data,” Bioinformatics, vol. 23, no. 15, pp. 1927–1935, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. G. Li, Q. Ma, H. Tang, A. H. Paterson, and Y. Xu, “QUBIC: a qualitative biclustering algorithm for analyses of gene expression data,” Nucleic Acids Research, vol. 37, no. 15, article e101, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. D. S. Rodriguez-Baena, A. J. Perez-Pulido, and J. S. Aguilar-Ruiz, “A biclustering algorithm for extracting bit-patterns from binary datasets,” Bioinformatics, vol. 27, no. 19, pp. 2738–2745, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Király, J. Abonyi, A. Laiho, and A. Gyenesei, “Biclustering of high-throughput gene expression data with bicluster miner,” in Proceedings of the 12th IEEE International Conference on Data Mining Workshops (ICDMW '12), pp. 131–138, Brussels, Belgium, December 2012. View at Publisher · View at Google Scholar