Abstract
In the past, we proposed a pre-large FUSP tree to preserve and maintain both large and pre-large sequences in the built tree structure. In this paper, the pre-large concept is also adopted for maintaining and updating the FUSP tree. Only large sequences are kept in the built tree structure for reducing computations. The PreFUSP-TREE-MOD maintenance algorithm is proposed to reduce the rescans of the original database due to the pruning properties of pre-large concept. When the number of modified sequences is smaller than the safety bound of the pre-large concept, better results can be obtained by the proposed PreFUSP-TREE-MOD maintenance algorithm for sequence modification in the dynamic database.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Agrawal, R., Imielinski, T., Swami, A.: Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5, 914–925 (1993)
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)
Agrawal, R., Srikant, R.: Quest synthetic data generator (1994), http://www.Almaden.ibm.com/cs/quest/syndata.html
Agrawal, R., Srikant, R.: Mining sequential patterns. In: The International Conference on Data Engineering, pp. 3–14 (1995)
Berkhin, P.: A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71 (2006)
Chen, M.S., Han, J., Yu, P.S.: Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8, 866–883 (1996)
Cheng, H., Yan, X., Han, J.: Incspan: Incremental mining of sequential patterns in large database. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 527–532 (2004)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 53–87 (2004)
Hong, T.P., Wang, C.Y., Tao, Y.H.: A new incremental data mining algorithm using pre-large itemsets. Intelligent Data Analysis 5, 111–129 (2001)
Hong, T.P., Wang, C.Y., Tseng, S.S.: An incremental mining algorithm for maintaining sequential patterns using pre-large sequences. Expert Systems with Applications 38, 7051–7058 (2011)
Huang, Z., Shyu, M.L., Tien, J.M., Vigoda, M.M., Birnbach, D.J.: Prediction of uterine contractions using knowledge-assisted sequential pattern analysis. IEEE Transactions on Biomedical Engineering 60, 1290–1297 (2013)
Kotsiantis, S.B.: Supervised machine learning: A review of classification techniques. In: The Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, pp. 3–24 (2007)
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)
Lin, C.W., Hong, T.P., Lu, W.H., Chen, H.Y.: An fusp-tree maintenance algorithm for record modification. In: IEEE International Conference on Data Mining Workshops, pp. 649–653 (2008)
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)
Lin, C.W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Systems with Applications 38, 7419–7424 (2011)
Lin, C.W., Hong, T.P.: A new mining approach for uncertain databases using cufp trees. Expert Systems with Applications 39, 4084–4093 (2012)
Lin, C.W., Lan, G.C., Hong, T.P.: An incremental mining algorithm for high utility itemsets. Expert Systems with Applications 39, 7173–7180 (2012)
Lin, C.W., Hong, T.P., Lee, H.Y., Wang, S.L.: Maintenance of pre-large FUSP trees in dynamic databases. In: International Conference on Innovations in Bio-inspired Computing and Applications, pp. 199–202 (2011)
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)
Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: The International Conference on Extending Database Technology: Advances in Database Technology, pp. 3–17 (1996)
Wang, C.Y., Hong, T.P., Tseng, S.S.: Maintenance of sequential patterns for record modification using pre-large sequences. In: IEEE International Conference on Data Mining, pp. 693–696 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, CR., Lin, CW., Gan, W., Hong, TP. (2014). A Modified Maintenance Algorithm for Updating FUSP Tree in Dynamic Database. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-07455-9_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07454-2
Online ISBN: 978-3-319-07455-9
eBook Packages: Computer ScienceComputer Science (R0)