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Efficient Algorithms for Finding Frequent Substructures from Semi-structured Data Streams

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New Frontiers in Artificial Intelligence (JSAI 2003, JSAI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3609))

Abstract

In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling-semistructured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in the document order through a data stream, and can return the current set of frequent patterns immediately on request at any time. We give modifications of the algorithm to other online mining models. Furthermore we implement our algorithms in different online models and candidate management strategies, then show empirical analyses to evaluate the algorithms.

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References

  1. Abe, K., Kawasoe, S., Asai, T., Arimura, H., Arikawa, S.: Optimized Substructure Discovery for Semi-structured Data. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 1–14. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  2. Abiteboul, S., Buneman, P., Suciu, D.: Data on the Web. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  3. Aho, A.V., Hopcroft, J.E., Ullman, J.D.: Data Structures and Algorithms. Addison-Wesley, Reading (1983)

    MATH  Google Scholar 

  4. Asai, T., Abe, K., Kawasoe, S., Arimura, H., Sakamoto, H., Arikawa, S.: Efficient Substructure Discovery from Large Semi-structured Data. In: Proc. SIAM SDM’02, pp. 158–174 (2002)

    Google Scholar 

  5. Asai, T., Arimura, H., Abe, K., Kawasoe, S., Arikawa, S.: Online Algorithms for Mining Semi-structured Data Stream. In: Proc. IEEE ICDM’02, pp. 27–34. IEEE Computer Society Press, Los Alamitos (2002)

    Google Scholar 

  6. Asai, T., Arimura, H., Uno, T., Nakano, S.: Discovering Frequent Substructures in Large Unordred Trees. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds.) DS 2003. LNCS (LNAI), vol. 2843, pp. 47–61. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  7. Bayardo Jr., R.J.: Efficiently Mining Long Patterns from Databases. In: Proc. SIGMOD98, pp. 85–93 (1998)

    Google Scholar 

  8. de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry, Algorithms and Applications. Springer, Heidelberg (2000)

    Book  Google Scholar 

  9. Dehaspe, L., Toivonen, H., King, R.D.: Finding Frequent Substructures in Chemical Compounds. In: Proc. KDD-98, pp. 30–36 (1998)

    Google Scholar 

  10. Gibbons, P.B., Matias, Y.: Synopsis Data Structures for Massive Data Sets. In: External Memory Algorithms. DIMACS Series in Discr. Math.  and Theor. Compt. Sci, vol. 50, pp. 39–70. AMS, New York (2000)

    Chapter  Google Scholar 

  11. Hidber, C.: Online Association Rule Mining. In: Proc. SIGMOD’99, pp. 145–156 (1999)

    Google Scholar 

  12. Kilpelainen, P., Mannila, H.: Ordered and Unordered Tree Inclusion. SIAM J. Comput. 24(2), 340–356 (1995)

    Article  MathSciNet  Google Scholar 

  13. Laird, P., Saul, R.: Discrete Sequence Prediction and Its Applications. Machine Learning 15(1), 43–68 (1994)

    MATH  Google Scholar 

  14. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering Frequent Episode in Sequences. In: Proc. KDD-95, pp. 210–215. AAAI, Menlo Park (1995)

    Google Scholar 

  15. Matsuda, T., Horiuchi, T., Motoda, H., Washio, T., Kumazawa, K., Arai, N.: Graph-Based Induction for General Graph Structured Data. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 340–342. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  16. Miyahara, T., Suzuki, Y., Shoudai, T., Uchida, T., Takahashi, K., Ueda, H.: Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 341–355. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  17. Nakano, S.: Efficient Generation of Plane Trees. Information Processing Letters 84, 167–172 (2002)

    Article  MathSciNet  Google Scholar 

  18. Parthasarathy, S., Zaki, M.J., Ogihara, M., Dwarkadas, S.: Incremental and Interactive Sequence Mining. In: CIKM’99, pp. 251–258 (1999)

    Google Scholar 

  19. Rastogi, R.: Single-Path Algorithms for Querying and Mining Data Streams. In: Proc. SDM’02 Workshop HDM’02, pp. 43–48 (2002)

    Google Scholar 

  20. W3C, Extensive Markup Language (XML) 1.0 (Second Edition), W3C Recommendation, 06 October (2000), http://www.w3.org/TR/REC-xml

  21. Wang, K., Liu, H.: Discovering Structural Association of Semistructured Data. TKDE 12(2), 353–371 (2000)

    Google Scholar 

  22. Yamanishi, K., Takeuchi, J.: A Unifying Framework for Detecting Outliers and Change Points from Non-Stationary Time Series Data. In: Proc.  SIGKDD-2002, ACM Press, New York (2002)

    Google Scholar 

  23. Zaki, M.J.: Efficiently mining frequent trees in a forest. In: Proc. SIGKDD-2002, ACM Press, New York (2002)

    Google Scholar 

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Authors and Affiliations

  1. Kyushu University, 6–10–1 Hakozaki Higashi-ku, Fukuoka 812–8581, Japan

    Tatsuya Asai, Kenji Abe, Shinji Kawasoe, Hiroki Arimura & Setsuo Arikawa

Authors
  1. Tatsuya Asai
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  2. Kenji Abe
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  3. Shinji Kawasoe
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  4. Hiroki Arimura
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  5. Setsuo Arikawa
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Editor information

Akito Sakurai Kôiti Hasida Katsumi Nitta

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Asai, T., Abe, K., Kawasoe, S., Arimura, H., Arikawa, S. (2007). Efficient Algorithms for Finding Frequent Substructures from Semi-structured Data Streams. In: Sakurai, A., Hasida, K., Nitta, K. (eds) New Frontiers in Artificial Intelligence. JSAI JSAI 2003 2004. Lecture Notes in Computer Science(), vol 3609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71009-7_3

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  • DOI: https://doi.org/10.1007/978-3-540-71009-7_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71008-0

  • Online ISBN: 978-3-540-71009-7

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