Abstract
A repeating pattern is a sequence composed of identical elements, repeating in a regular manner. In real life, there are lots of applications such as musical and medical sequences containing valuable repeating patterns. Because the repeating patterns hidden in sequences might contain implicit knowledge, how to retrieve the repeating patterns effectively and efficiently has been a challenging issue in recent years. Although a number of past studies were proposed to deal with this issue, the performance cannot still earn users’ satisfactions especially for large datasets. To aim at this issue, in this paper, we propose an efficient algorithm named Fast Mining of Repeating Patterns (FMRP), which achieves high performance for finding repeating patterns by a novel index called Quick-Pattern-Index (QPI). This index can provide the proposed FMRP algorithm with an effective support due to its information of pattern positions. Without scanning a given sequence iteratively, the repeating patterns can be discovered by only one scan of the sequence. The experimental results reveal that our proposed algorithm performs better than the compared methods in terms of execution time.
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References
Cooper, M., Foote, J.: Automatic music summarization via similarity analysis. In: The 2002 IEEE International Conference on Music Information Retrieval, pp. 81–85 (2002)
Friedman, S., Stamos, I.: Online detection of repeated structures in point clouds of urban scenes for compression and registration. Int. J. Comput. Vis. 102(1–3), 112–128 (2013)
Gool, L.V., Zeng, G., Wonka, P., Muller, P.: Image-based procedural modeling of facades. In: The ACM SIGGRAPH Conference on Computer Graphics, pp. 63–130 (2007)
Han, B.J., Hwang, E., Rho, S.: An efficient voice transcription scheme for music retrieval. In: The 2007 IEEE International Conference on Multimedia and Ubiquitous Engineering, pp. 28–26 (2007)
Hsu, J.L., Liu, C.C., Chen, L.P.: Discovering non-trivial repeating patterns in music data. IEEE Trans. Multimedia 3(3), 311–325 (2001)
Liu, J., Psarakis, E., Stamos, I.: Automatic kronecker product model based detection of repeated patterns in 2D Urban Images. In: The 2013 IEEE International Conference on Computer Vision, pp. 401–408 (2013)
Ma, Y.F., Lu, L., Zhang, H.J., Li, M.J.: A user attention model for video summarization. In: The Tenth ACM International Conference on Multimedia, pp. 533–542 (2002)
Singh, A.: Ukkonen’s suffix tree construction (2014). http://www.geeksforgeeks.org/ukkonens-suffix-tree-construction-part-6/
Ukkonen, E.: On-line construction of suffix tree. Algorithmica 14(3), 249–260 (1995)
Wang, M., Lu, L., Zhang, H.H.: Repeating pattern discovery from acoustic musical signals. In: The 2004 IEEE International Conference on Multimedia and Expo, vol. 3, pp. 2019–2022 (2004)
Xiao, R.G., Wang, Y.Y., Pan, H., Wu, F.: Automatic video summarization by spatio-temporal analysis and non-trivial repeating pattern detection. In: The 2008 IEEE Congress on Image and Signal Processing, pp. 555–559 (2008)
Zhao, P., Fang, T., Xiao, J., Zhang, H., Zhao, Q., Quan, L.: Rectilinear parsing of architecture in Urban environment. In: The 2010 IEEE Computer Vision and Pattern Recognition, pp. 342–349 (2010)
Acknowledgements
This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 105-2221-E-230-011-MY2 and MOST 105-2632-S-424-001.
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Su, JH., Hong, TP., Chin, CY., Liao, ZF., Cheng, SY. (2017). A High-Performance Algorithm for Mining Repeating Patterns. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_59
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DOI: https://doi.org/10.1007/978-3-319-54472-4_59
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