Abstract
In this paper, an efficient strategy for mining top-K non-trivial fault-tolerant repeating patterns (FT-RPs in short) with lengths no less than min_len from data sequences is provided. By extending the idea of appearing bit sequences, fault-tolerant appearing bit sequences are defined to represent the locations where candidate patterns appear in a data sequence with insertion/deletion errors being allowed. Two algorithms, named TFTRP-Mine(Top-K non-trivial FT-RPs Mining) and RE-TFTRP-Mine (REfinement of TFTRP-Mine), respectively, are proposed. Both of these two algorithms use the recursive formulas to obtain the fault-tolerant appearing bit sequence of a pattern systematically and then the fault-tolerant frequency of each candidate pattern could be counted quickly. Besides, RE-TFTRP-Mine adopts two additional strategies for pruning the searching space in order to improve the mining efficiency. The experimental results show that RE-TFTRP-Mine outperforms TFTRP-Mine algorithm when K and min_len are small. In addition, more important and implicit repeating patterns could be found from real music objects by adopting fault tolerant mining.
This work was partially supported by the R.O.C. N.S.C. under Contract No. 94-2213- E-003-010.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Liu, C.C., Hsu, J.L., Chen, A.L.P.: Efficient Theme and Non-Trivial Repeating Pattern Discovering in Music Databases. In: Proceedings of the 15th International Conference on Data Engineering (ICDE 1999) (1999)
Han, J., Wang, J., Lu, Y., Tzvetkov, P.: Mining Top-K Frequent Closed Patterns without Minimum Support. In: Proceedings of 2002 International Conference on Data Mining (ICDM 2002) (2002)
Hsu, J.L., Liu, C.C., Chen, A.L.P.: Efficient Repeating Pattern Finding in Music Databases. In: Proceedings of the Seventh International Conference on Information and Knowledge Management(ACM CIKM 1998) (1998)
Koh, J.L., Yu, W.D.C.: Efficient Feature Mining in Music Objects. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 221–231. Springer, Heidelberg (2001)
Koh, J.L., Yo, P.W.: An Efficient Approach for Mining Fault-Tolerant Frequent Itemsets based on Bit Sequences. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, Springer, Heidelberg (2005)
Pei, J., Tung, A.K.H., Han, J.: Fault-Tolerant Frequent Pattern Mining: Problem and Challenges. In: Proceedings of ACM-SIGMOD International Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD 2001) (2001)
Tzvetkov, P., Yan, X., Han, J.: TSP: Mining Top-K Closed Sequential Patterns. In: Proceedings of 2003 International Conference on Data Mining (ICDM 2003) (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koh, JL., Kung, YT. (2006). An Efficient Approach for Mining Top-K Fault-Tolerant Repeating Patterns. In: Li Lee, M., Tan, KL., Wuwongse, V. (eds) Database Systems for Advanced Applications. DASFAA 2006. Lecture Notes in Computer Science, vol 3882. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11733836_9
Download citation
DOI: https://doi.org/10.1007/11733836_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33337-1
Online ISBN: 978-3-540-33338-8
eBook Packages: Computer ScienceComputer Science (R0)