Skip to main content
SpringerLink
Log in
Book cover

Mining of Data with Complex Structures pp 23–40Cite as

Tree Mining Problem

  • Chapter

  • 805 Accesses

Part of the book series: Studies in Computational Intelligence ((SCI,volume 333))

Abstract

As mentioned in Chapter 1, an important category of complex data is tree-structured data. It occurs in a variety of different domains and applications such as Web Intelligence applications, bioinformatics, natural language processing, programming compilation, scientific knowledge management and querying, etc. (Wang et al. 1994). Mining of tree-structured data introduces significant new challenges which are the subject of this chapter.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imieliski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington D.C., USA, May 26-28, pp. 207–216. ACM, New York (1993)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), Santiago de Chile, Chile, Septemebr 12-15, pp. 487–499 (1994)

    Google Scholar 

  3. Agrawal, R., Srikant, R.: Mining sequential patterns. Paper presented at the Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan, March 6-10 (1995)

    Google Scholar 

  4. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Usama, M.F., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining. American Association for Artificial Intelligence, pp. 307–328 (1996)

    Google Scholar 

  5. Bayardo, R.J.: Efficiently mining long patterns from databases. Paper presented at the Proceedings of the ACM SIGMOD Conference on Management of Data, Seattle, USA, June 2-4 (1998)

    Google Scholar 

  6. Bayardo, R.J., Agrawal, R., Gunopulos, D.: Constraint-based rule mining on large, dense data sets. Paper presented at the Proceedings of the 15th International Conference on Data Engineering Sydeny, Australia, March 23-26 (1999)

    Google Scholar 

  7. Brin, S., Motwani, R., Silverstein, C.: Beyond Market Baskets: Generalizing Association Rules to Correlations. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Tucson, Arizona, USA, May 13-15, pp. 265–276. ACM, New York (1997)

    Chapter  Google Scholar 

  8. Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Data. Paper presented at the Proceedings of the, ACM SIGMOD International Conference on Management of Data, Tucson, Arizona, USA, May 13-15 (1997)

    Google Scholar 

  9. Chen, M.S., Han, J., Yu, P.S.: Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8, 866–883 (1996)

    Article  Google Scholar 

  10. Chi, Y., Yang, Y., Muntz, R.R.: Canonical forms for labeled trees and their applications in frequent subtree mining. Knowledge and Information Systems 8(2), 203–234 (2004)

    Article  Google Scholar 

  11. Chi, Y., Muntz, R.R., Nijssen, S., Kok, J.N.: Frequent Subtree Mining - An Overview. Fundamenta Informaticae, Special Issue on Graph and Tree Mining 66(1-2), 161–198 (2005)

    MATH  MathSciNet  Google Scholar 

  12. Clark, P., Boswell, P.: Rule Induction with CN2: Some Recent Improvements. Paper presented at the Proceedings of the 5th European Machine Learning Conference, Porto, Portugal, March 6-8 (1991)

    Google Scholar 

  13. Desitel, R.: Graph Theory, 3rd edn. Heidelberg Graduate Texts in Mathematics, vol. 173. Springer, New York (2000)

    Google Scholar 

  14. Dhar, V., Tuzhilin, A.: Abstract-Driven Pattern Discovery in Databases. IEEE Transactions on Knowledge and Data Engineering 5(6), 926–938 (1993)

    Article  Google Scholar 

  15. Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. Paper Presented at the Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 1999), San Diego, CA, USA, August 15-18 (1999)

    Google Scholar 

  16. Ezeife, C.I., Lu, Y.: Mining Web Log sequential Patterns with Position Coded Pre-Order Linked WAP-tree. Data Mining and Knowledge Discovery 10(1), 5–38 (2005)

    Article  MathSciNet  Google Scholar 

  17. Feng, L., Dillon, T.S., Weigand, H., Chang, E.: An XML-Enabled Association Rule Framework. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 88–97. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  18. Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T.: Data Mining using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization. Paper presented at the Proceedings of the 1996 ACM-SIGMOD International Conference on Management of Data, Motreal, Canada, June 4-6 (1996)

    Google Scholar 

  19. Han, J., Dong, G., Yin, Y.: Efficient mining of partial periodic patterns in time series database. Paper presented at the Proceedings of the 15th International Conference on Data Engineering sydeny, Australia, March 23-26 (1999)

    Google Scholar 

  20. Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  21. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Elsevier, Morgan Kaufmann Publishers, San Francisco, CA, USA (2006)

    Google Scholar 

  22. IBM, IBM Intelligent Miner User’s Guide, Version 1, Release 1 (1996)

    Google Scholar 

  23. Kamber, M., Han, J., Chiang, J.Y.: Metarule-guided mining of multi-dimensional association rules using data cubes. Paper presented at the Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, USA, August 14-17 (1997)

    Google Scholar 

  24. Lent, B., Swami, A., Widom, J.: Clustering association rules. Paper presented at the Proceedings of the 13th International Conference on Data Engineering, Birmingham, UK, April 7-11 (1997)

    Google Scholar 

  25. Mannila, H., Toivonen, H., Verkamo, A.I.: Efficient algorithms for discovering association rules. Paper presented at the Proceedings of AAAI 1994 Workshop on Knowledge Discovery in Databases Seattle, WA, USA, July 31- August 4 (1994)

    Google Scholar 

  26. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)

    Article  Google Scholar 

  27. Morimoto, Y., Fukuda, T., Matsuzawa, H., Tokuyama, T., Yoda, K.: Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases. Paper presented at the Proceedings of the 24th International Conference on Very Large Databases (VLDB), New York City, NY, USA, August 24-27 (1998)

    Google Scholar 

  28. Morishita, S.: On Classification and Regression. Paper presented at the Proceedings of the 1st International Conference on Discovery Science, Fukuoka, Japan, December 14-16 (1998)

    Google Scholar 

  29. Nakaya, A., Morishita, S.: Parallel Branch-and-Bound Graph Search for Correlated Association Rules. In: Zaki, M.J., Ho, C.-T. (eds.) KDD 1999. LNCS (LNAI), vol. 1759, pp. 127–144. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  30. Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 229–238. AAAI/MIT Press (1991)

    Google Scholar 

  31. Silverstein, C., Brin, S., Motwani, R., Ullman, J.: Scalable techniques for mining causal structures. Paper presented at the Proceedings of the 24th International Conference on Very Large Databases (VLDB), New York City, NY, USA, August 24-27 (1998)

    Google Scholar 

  32. Tan, H., Hadzic, F., Feng, L., Chang, E.: MB3-Miner: mining eMBedded subTREEs using tree model guided candidate generation. In: Proceedings of the 1st International Workshop on Mining Complex Data in Conjunction with ICDM 2005, Houston, Texas, USA, November 27-30, pp. 103–110 (2005)

    Google Scholar 

  33. Tan, H., Dillon, T.S., Hadzic, F., Chang, E.: SEQUEST: Mining Frequent Subsequences using DMA Strips. Paper presented at the Proceeding of the 7th International Conference on Data Mining and Information Engineering, Prague, Czech Republic, July 11-13 (2006)

    Google Scholar 

  34. Valentine, G.: Algorithms on Trees and Graphs. Springer, Berlin (2002)

    Google Scholar 

  35. Wang, J.T., Zhang, K., Jeong, K., Shasha, D.: A System for Approximate Tree Matching. IEEE Transactions on Knowledge and Data Engineering 6(4), 559–571 (1994)

    Article  Google Scholar 

  36. Webb, G.I.: OPUS: An Efficient Admissible Algorithm for Unordered Search. Journal of Artificial Intelligence Research 3, 431–465 (1995)

    MATH  Google Scholar 

  37. Zaki, M.J.: Fast mining of sequential patterns in very large databases. University of Rochester Computer Science Department, New York (1997)

    Google Scholar 

  38. Zaki, M.J., Ogihara, M.: Theoretical Foundations of Association Rules. Paper presented at the Proceedings of the 3rd ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Seattle, Washington, USA, June 2-4 (1998)

    Google Scholar 

  39. Zaki, M.J.: Scalable Algorithms for Association Mining. IEEE Transactions on Knowledge and Data Engineering 12(3), 372–390 (2000)

    Article  MathSciNet  Google Scholar 

  40. Zaki, M.J., Lesh, N., Ogihara, M.: PlanMine: Predicting Plan Failures Using Sequence Mining. Artificial Intelligence Review 14(6), 421–446 (2000)

    Article  MATH  Google Scholar 

  41. Zaki, M.J.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning 42(1/2), 31–60 (2001)

    Article  MATH  Google Scholar 

  42. Zaki, M.J., Hsiao, C.-J.: CHARM: An Efficient Algorithm for Closed Itemsets Mining. Paper presented at the Proceedings of the 2nd SIAM International Conference on Data Mining, Arlington, VA, USA, April 11-13 (2002)

    Google Scholar 

  43. Zaki, M.J.: Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications. IEEE Transactions on Knowledge and Data Engineering 17(8), 1021–1035 (2005)

    Article  Google Scholar 

Download references

Authors
  1. Fedja Hadzic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Henry Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tharam S. Dillon
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hadzic, F., Tan, H., Dillon, T.S. (2011). Tree Mining Problem. In: Mining of Data with Complex Structures. Studies in Computational Intelligence, vol 333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17557-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17557-2_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17556-5

  • Online ISBN: 978-3-642-17557-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation