Extraction of Interesting Financial Information from Heterogeneous XML-Based Data

  • Juryon Paik
  • Young Ik Eom
  • Ung Mo Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)


XML is going to be the main language for exchanging financial information between businesses over the Internet. As more and more banks and financial institutions move to electronic information exchange and reporting, the financial world is in a flood of information. With the sheer amount of financial information stored, presented and exchanged using XML-based standards, the ability to extract interesting knowledge from the data sources to better understand customer buying/selling behaviors and upward/downward trends in the stock market becomes increasingly important and desirable. Hence, there have been growing demands for efficient methods of discovering valuable information from a large collection of XML-based data. One of the most popular approaches to find the useful information is to mine frequently occurring tree patterns. In this paper, we propose a novel algorithm, FIXiT,for efficiently extracting maximal frequent subtrees from a set of XML-based documents. The main contributions of our algorithm are that: (1) it classifies the available financial XML standards such as FIXML, FpML, XBRL, and so forth with respect to their specifications, and (2) there is no need to perform tree join operations during the phase of generating maximal frequent subtrees.


Minimum Support Mining Association Rule Label Tree Tree Pruning Semistructured Data 


  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 12th International Conference on Very Large Databases, pp. 487–499 (1994)Google Scholar
  2. 2.
    Asai, T., Abe, K., Kawasoe, S., Arimura, H., Sakamoto, H., Arikawa, S.: Efficient substructure discovery from large semi-structured data. In: Proceedings of the 2nd SIAM International Conference on Data Mining, pp. 158–174 (2002)Google Scholar
  3. 3.
    Chi, Y., Yang, Y., Muntz, R.R.: HybridTreeMiner: An efficient algorithm for mining frequent rooted trees and free trees using canonical forms. In: The 16th International Conference on Scientific and Statistical Database Management, pp. 11–20 (2004)Google Scholar
  4. 4.
    Chi, Y., Yang, Y., Muntz, R.R.: Canonical forms for labelled trees and their applications in frequent subtree mining. Knowledge and Information Systems 8(2), 203–234 (2005)CrossRefGoogle Scholar
  5. 5.
    Inokuchi, A., Washio, T., Motoda, H.: An Apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of IEEE International Conference on Data Mining, pp. 313–320 (2001)Google Scholar
  7. 7.
    Miyahara, T., Suzuki, T., 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)CrossRefGoogle Scholar
  8. 8.
    Paik, J., Shin, D.R., Kim, U.M.: EFoX: a Scalable Method for Extracting Frequent Subtrees. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 813–817. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Paik, J., Won, D., Fotouhi, F., Kim, U.M.: EXiT-B: A New Approch for Extracting Maximal Frequent Subtrees from XML Data. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 1–8. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Termier, A., Rousset, M.-C., Sebag, M.: TreeFinder: a First step towards XML data mining. In: Proceedings of IEEE International Conference on Data Mining, pp. 450–457 (2002)Google Scholar
  11. 11.
    Wang, K., Liu, H.: Schema discovery for semistructured data. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 271–274 (1997)Google Scholar
  12. 12.
    Zaki, M.J.: Efficiently mining frequent trees in a forest. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 71–80 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Juryon Paik
    • 1
  • Young Ik Eom
    • 1
  • Ung Mo Kim
    • 1
  1. 1.Department of Computer EngineeringSungkyunkwan UniversityGyeonggi-doRepublic of Korea

Personalised recommendations