An Incremental Algorithm for Maintaining the Built FUSP Trees Based on the Pre-large Concepts
Mining useful information or knowledge from a very large database to aid managers or decision makers to make appropriate decisions is a critical issue in recent years. In this paper, we adopted the pre-large concepts to the FUSP-tree structure for sequence insertion. A FUSP tree is built in advance to keep the large 1-sequences for later maintenance. The pre-large sequences are also kept to reduce the movement from large to small and vice versa. When the number of inserted sequences is smaller than the safety bound of the pre-large concepts, better results can be obtained by the proposed incremental algorithm for sequence insertion in dynamic databases.
KeywordsPre-large concept dynamic databases sequential pattern mining sequence insertion FUSP-tree structure
Unable to display preview. Download preview PDF.
- 2.Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
- 3.Agrawal, R., Srikant, R.: Mining sequential patterns. In: The International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
- 5.Wang, C.Y., Hong, T.P., Tseng, S.S.: Maintenance of sequential patterns for record deletion. In: IEEE International Conference on Data Mining, pp. 536–541 (2001)Google Scholar
- 11.Lin, C.W., Hong, T.P., Lu, W.H., Lin, W.Y.: An incremental fusp-tree maintenance algorithm. In: The International Conference on Intelligent Systems Design and Applications, pp. 445–449 (2008)Google Scholar
- 12.Lin, C.W., Hong, T.P., Lu, W.H.: An efficient fusp-tree update algorithm for deleted data in customer sequences. In: International Conference on Innovative Computing, Information and Control, pp. 1491–1494 (2009)Google Scholar
- 13.Lin, C.W., Hong, T.P.: A survey of fuzzy web mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, 190–199 (2013)Google Scholar
- 14.Lin, M.Y., Lee, S.Y.: Incremental update on sequential patterns in large databases. In: IEEE International Conference on Tools with Artificial Intelligence, pp. 24–31 (1998)Google Scholar
- 15.Nakagaito, F., Ozaki, T., Ohkawa, T.: Discovery of quantitative sequential patterns from event sequences. In: IEEE International Conference on Data Mining Workshops, pp. 31–36 (2009)Google Scholar
- 18.Zheng, Z., Kohavi, R., Mason, L.: Real world performance of association rule algorithms. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 401–406 (2001)Google Scholar