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Processing Sequential Patterns in Relational Databases

  • Xuequn Shang
  • Kai-Uwe Sattler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4380)

Abstract

Integrating data mining techniques into database systems has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specialized implementations. Reasons for this are among others the prohibitive nature of the cost associated with extracting knowledge as well as the lack of suitable declarative query language support. Recent studies have found that for association rule mining and sequential pattern mining with carefully tuned SQL formulations it is possible to achieve performance comparable to systems that cache the data in files outside the DBMS. However, most of the previous pattern mining methods follow the method of Apriori, which still encounters problems when a sequential database is large and/or when sequential patterns to be mined are numerous and long.

In this paper, we present a novel SQL based approach that we recently proposed, called Prospad (PROjection Sequential PAttern Discovery). Prospad fundamentally differs from an Apriori-like candidate set generation-and-test approach. This approach is a pattern growth-based approach without candidate generation. It grows longer patterns from shorter ones by successively projecting the sequential table into subsequential tables. Since a projected table for a sequential pattern i contains all and only necessary information for mining the sequential patterns that can grow from i, the size of the projected table usually reduces quickly as mining proceeds to longer patterns. Moreover, a depth first approach is used to facilitate the projecting process in order to avoid creating and dropping costs of temporary tables.

Keywords

Sequential Pattern Frequent Pattern Pattern Mining Association Rule Mining Frequent Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential Pattern Mining using a Bitmap Representation. In: Knowledge Discovery and Data Mining. 8th Int. Conference, pp. 429–435. ACM Press, New York, NY, USA (2002)Google Scholar
  2. 2.
    Antunes, C., Oliveira, A.L.: Sequential Pattern Mining Algorithms: Trade-offs between Speed and Memory. In: 2nd Int. Workshop on Mining Graphs, Trees and Sequences, Pisa, Italy, (September 2004)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Data Engineering (ICDE’95). 11th Int. Conference, Taipei, Taiwan, pp. 3–14. IEEE Computer Society Press (1995)Google Scholar
  4. 4.
    Chaudhuri, S.: Data Mining and Database Systems: Where is the Intersection? Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 21(1) (March 1998)Google Scholar
  5. 5.
    Han, J., Fu, Y., Wang, W.: DMQL: A Data Mining Query Language for Relational Database. In: Proc. of the 1996 SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada (1996)Google Scholar
  6. 6.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: Proc. of the Int. Conf. on Data Engineering (ICDE’01), pp. 215–224, Heidelberg, Germany (April 2001)Google Scholar
  7. 7.
    Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Berlin Heidelberg New York (1996)CrossRefGoogle Scholar
  8. 8.
    Shang, X., Sattler, K.: Depth-First Frequent Itemset Mining in Relational Databases. In: Proc. ACM Symposium on Applied Computing SAC 2005, New Mexico, USA (2005)Google Scholar
  9. 9.
    Sarawagi, S., Thomas, S., Agrawal, R.: Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications. In: Proc. of the Int. Conf. on Management of Data (SIGMOD’98), pp. 345–354. ACM Press, New York (1998)Google Scholar
  10. 10.
    Toivonen, H.: Sampling Large Databases for Association Rules. In: Proc. of Int. Conf. Very Large Data Bases (VLDB’96), pp. 134–145 (1996)Google Scholar
  11. 11.
    Thomas, S., Sarawagi, S.: Mining Generalized Association Rules and Sequential Patterns Using SQL Queries. In: Proc. of the 4th Int. Conference on Knowledge Discovery and Data Mining (KDD-98), pp. 344–348 (1998)Google Scholar
  12. 12.
    Wojciechowski, M.: Mining Various Patterns in Sequential Data in an SQL-like Manner. In: Advances in Databases and Information Systems, 3rd East European Conference (ADBIS’99A) – Short Papers, pp. 131–138 (1999)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Xuequn Shang
    • 1
  • Kai-Uwe Sattler
    • 2
  1. 1.School of Computer Science, Northwestern Polytechnical University, 710072, ShaanxiChina
  2. 2.Department of Computer Science and Automation, Technical University of IlmenauGermany

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