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Discovering Tendency Association between Objects with Relaxed Periodicity and Its Application in Seismology

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Internet Applications (ICSC 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1749))

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Abstract

Relaxed periodicity is proposed to describe loose-‘cyclic behavior of objects while allowing uneven stretch or shrink on time axis, limited noises, and inflation/deflation of attribute values. The techniques to mine the relaxed periodicity and the association between objects with relaxed periodicity are studied. The proposed algorithms are tested by the data in the Seismic database of Annin River area, and its results are interesting to seismology.

This project is in part supported by The National Science Foundation of China grant, #69773051 and the Hong Kong CERG grant, #9040339.

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References

  1. Tang, C., Yu, Z., Zhang, T.: Discover Relaxed Periodicity In Temporal Databases. In: Proc. of the Int. Conference on Database System for Advanced Application (April 1999)

    Google Scholar 

  2. Han, J., Gong, W., Yin, Y.: Mining Segment-wise Periodic Pattern in Time Related Databases. In: Proc. of the International Conference on Knowledge Discovery and Data Mining, pp. 7–181 (August 1998)

    Google Scholar 

  3. Han, J., Dong, G., Yin, Y.: Efficient Mining of Partial Periodic Patterns in Time Series Database. In: 15th International Conference on Data Engineering (ICDE 1999), Sydney, Australia, March 23-26 (1999)

    Google Scholar 

  4. Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic Association Rules. In: Proc. of IEEE International Conference Data Engineering, pp. 412–421 (1998)

    Google Scholar 

  5. Agrawal, R., Ling, K., Sawhney, H., Shim, K.: Fast Similarity Search in the Presence of Inertia, Scaling, and Translation in Time-Series Databases. In: Proc. of VLDB (1995)

    Google Scholar 

  6. Sakoe, H., Chiba, S.: Dynamic Programming Algorithm Optimization for Spoken Word Recognition. In: Waibel, A., Lee, K.-F. (eds.) Readings In speech Recognition, pp. 159–165. Morgan Kaufman, San Francisco (1990)

    Google Scholar 

  7. Tansel, A., et al.: Temporal Databases - Theory, Design and Implementation, pp. 418–455. The Benjamin/Cummings Publishing Company (1997)

    Google Scholar 

  8. Fayyad, U., Piatetsky-Shapiro, G.: Advanced in Knowledge Discover and Data Mining. AAAI Press and The MIT Press (1996)

    Google Scholar 

  9. Wang, X., Zhang, C., Pen, X.: Basic Segmentation Characteristics on Late Quaternary Anninghe Active Faults. In: Earthquake Research in Sichuan, pp. 51-61 (1998)

    Google Scholar 

  10. Cheng, W.: The Association Between Active Statistics Seismic Data. In: The Research of Medium-term and Short-term Seismic Prediction in Sichuan, pp. 49–55. Chengdu Map Publishing House (1994)

    Google Scholar 

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Tang, C. et al. (1999). Discovering Tendency Association between Objects with Relaxed Periodicity and Its Application in Seismology. In: Hui, L.C.K., Lee, DL. (eds) Internet Applications. ICSC 1999. Lecture Notes in Computer Science, vol 1749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46652-9_6

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  • DOI: https://doi.org/10.1007/978-3-540-46652-9_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66903-6

  • Online ISBN: 978-3-540-46652-9

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