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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.

참고문헌 (12)

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  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. 
  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. 
  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. 
  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. 
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