DOI QR코드

DOI QR Code

IMTAR: Incremental Mining of General Temporal Association Rules

  • Dafa-Alla, Anour F.A. (Database/Bioinformatics Lab., Chungbuk National University) ;
  • Shon, Ho-Sun (Database/Bioinformatics Lab., Chungbuk National University) ;
  • Saeed, Khalid E.K. (Database/Bioinformatics Lab., Chungbuk National University) ;
  • Piao, Minghao (Database/Bioinformatics Lab., Chungbuk National University) ;
  • Yun, Un-Il (School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Cheoi, Kyung-Joo (School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Ryu, Keun-Ho (Database/Bioinformatics Lab., Chungbuk National University)
  • Received : 2010.02.02
  • Accepted : 2010.04.06
  • Published : 2010.06.30

Abstract

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

References

  1. R. Agrawal, T. Imielinski, A. Swan, “Mining Association Rules Between Set of Items in Large Databases”, In ACM SIGMOD International Conference on Management of Data, pp.207-216, 1995. https://doi.org/10.1145/170035.170072
  2. D. W., Cheung, J. Han, V. Neg, Y. Wong, “Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique”, In the International Conference on Data Engineering, pp.106-114, 1996. https://doi.org/10.1109/ICDE.1996.492094
  3. W. Wang, Y. Yang, R. Muntz, “Temporal Association Rules with Numeric Attribute”, In NCLA CSD Technical Report. 1999.
  4. W. Wang, Y. Yang, R. Muntz, R., “Temporal Association Rules on Evolving Numerical Attribute”, In the 17th International Conference on Data Engineering, pp.283-292, 2001. https://doi.org/10.1109/ICDE.2001.914839
  5. C.H. Lee, M.S. Chen, C.R. Lin, C, R., “Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules”, In IEEE Transaction on Knowledge Engineering, pp.1004-1017, 2003. https://doi.org/10.1109/TKDE.2003.1209015
  6. F. Conen, P. Leng, S. Ahmed, “Data Structure for Association Rules Mining: T-tree and P-tree”, In IEEE Transaction on Knowledge Engineering, pp.774-778, 2004. https://doi.org/10.1109/TKDE.2004.8
  7. R. Rymon, “Searching through systematic set enumeration”, In the 3rd International Conference on Principles of Knowledge and Reasoning, 1993.
  8. C. Zheng, “An Incremental updating technique for mining indirect association rules,” Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, pp.217-221, 2008. https://doi.org/10.1109/ICMLC.2008.4620407
  9. X. Li, Z.H. Deng and S. Twang, “A Fast Algorithm for Maintenance of Association Rules in Incremental Databases” Adnaced Data Mining and Application, pp.56-63, 2006. https://doi.org/10.1007/11811305_5
  10. M. Adnan, R. Alhajj, and K. Barker “Constructing Complete FP-Tree for Incremental Mining of Frequent Patterns in Dynamic Databases”, Advances in Applied Artificial Intelligence, 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE, pp.363-372, 2006. https://doi.org/10.1007/11779568_40
  11. W. Cheung, and O. R. Zaiane, “Incremental Mining of Frequent Patterns without Candidate Generation or Support Constraint”, in Proc. IDEAS’03, 2003, vol. 1098-8068/03, p.111.
  12. J. S. Park, M. S. Chen and P.S. Yu, “An Effective Hashed-Based Algorithm for Mining Association Rules”, in proc. 1995 ACM-SIGMOD Int. Conf. Management of Data, San Joe, CA. May, 1995. https://doi.org/10.1145/568271.223813

Cited by

  1. Effect of Temporal Relationships in Associative Rule Mining for Web Log Data vol.2014, 2014, https://doi.org/10.1155/2014/813983
  2. Incremental mining of weighted maximal frequent itemsets from dynamic databases vol.54, 2016, https://doi.org/10.1016/j.eswa.2016.01.049