Abstract
Recently, mining compact frequent patterns (for example closed patterns and compressed patterns) has received much attention from data mining researchers. These studies try to address the interpretability and efficiency problems encountered by traditional frequent pattern mining methods. However, to the best of our knowledge, how to efficiently mine compact sequential patterns in a dynamic sequence database environment has not been explored yet.
In this paper, we examine the problem how to mine closed sequential patterns incrementally. A compact structure CSTree is designed to keep the closed sequential patterns, and an efficient algorithm IMCS is developed to maintain the CSTree when the sequence database is updated incrementally. A thorough experimental study shows that IMCS outperforms the state-of-the-art algorithms – PrefixSpan, CloSpan, BIDE and a recently proposed incremental mining algorithm IncSpan by about a factor of 4 to more than an order of magnitude.
This work is supported by the NSFC Grants 60473051, 60642004 and 60473072, and the National High Technology Research and Development Program of China Grant 2006AA01Z230.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential PAttern Mining using A Bitmap Representation. In: Int. Conf. on Knowledge Discovery and Data Mining (2002)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Int. Conf. on Data Engineering (1995)
Agrawal, R., Srikant, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Int. Conf. on Extending Database Technology (1996)
Cheng, H., Yan, X., Han, J.: IncSpan: Incremental Mining of Sequential Patterns in Large Database. In: Int. Conf. on Knowledge Discovery and Data Mining (2004)
Chang, L., Yang, D.-q., Tang, S.-w., Wang, T.: Mining Compressed Sequential Patterns. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 761–768. Springer, Heidelberg (2006)
Kao, B., Zhang, M., Yip, C., Cheung, D.W.: Efficient Algorithms for Mining and Incremental Update of Maximal Frequent Sequences. Data Mining and Knowledge Discovery (2005)
Lin, M., Lee, S.: Incremental Update on Sequential Patterns in Large Databases by Implicit Merging and Efficient Counting. Information System (2004)
Masseglia, F., Poncelet, P., Teisseire, M.: Incremental Mining of Sequential Patterns in Large Databases. Data & Knowledge Engineering (2003)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: Int. Conf. on Data Engineering (2001)
Parthasarathy, S., Zaki, M.J., Ogihara, M., Dwarkadas, S.: Incremental and Interactive Sequence Mining. In: Int. Conf. on Information and Knowledge Management (1999)
Xin, D., Han, J., Yan, X., Cheng, H.: Mining Compressed Frequent-Pattern Sets. In: Int. Conf. on Very Large Data Bases (2005)
Yan, X., Han, J., Afshar, R.: CloSpan: Mining Closed Sequential Patterns in Large Datasets. In: SIAM Int. Conf. on Data Mining (2003)
Yan, X., Cheng, H., Han, J., Xin, D.: Summarizing Itemset Patterns: A Profile-Based Approach. In: Int. Conf. on Knowledge Discovery and Data Mining (2005)
Wang, J., Han, J.: BIDE: Efficient Mining of Frequent Closed Sequences. In: Int. Conf. on Data Engineering (2004)
Zaki, M.J.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Chang, L., Yang, D., Wang, T., Tang, S. (2007). IMCS: Incremental Mining of Closed Sequential Patterns . In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-72524-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72483-4
Online ISBN: 978-3-540-72524-4
eBook Packages: Computer ScienceComputer Science (R0)