Skip to main content
SpringerLink
Log in

Implicit Enumeration of Patterns

  • Conference paper
Knowledge Discovery in Inductive Databases (KDID 2004)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3377))

Included in the following conference series:

Abstract

Condensed representations of pattern collections have been recognized to be important building blocks of inductive databases, a promising theoretical framework for data mining, and recently they have been studied actively. However, there has not been much research on how condensed representations should actually be represented.

In this paper we study implicit enumeration of patterns, i.e., how to represent pattern collections by listing only the interestingness values of the patterns. The main problem is that the pattern classes are typically huge compared to the collections of interesting patterns in them. We solve this problem by choosing a good ordering of listing the patterns in the class such that the ordering admits effective pruning and prediction of the interestingness values of the patterns. This representation of interestingness values enables us to quantify how surprising a pattern is in the collection. Furthermore, the encoding of the interestingness values reflects our understanding of the pattern collection. Thus the size of the encoding can be used to evaluate the correctness of our assumptions about the pattern collection and the interestingness measure.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C, May 26-28, pp. 207–216. ACM Press, New York (1993)

    Chapter  Google Scholar 

  2. Goethals, B., Zaki, M.J. (eds.): Proceedings of the Workshop on Frequent Itemset Mining Implementations (FIMI 2003), Melbourne Florida, USA, November 19. CEUR Workshop Proceedings, vol. 90 (2003), http://CEUR-WS.org/Vol-90/

  3. Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: Mining graph data. Machine Learning 50, 321–354 (2003)

    Article  MATH  Google Scholar 

  4. Kurakochi, M., Karypis, G.: Discovering frequent geometric subgraphs. In: [32], pp. 258–265

    Google Scholar 

  5. Wang, X., Wang, J.T., Shasha, D., Shapiro, B.A., Rigoutsos, I., Zhang, K.: Finding patterns in three-dimensional graphs: Algorithms and applications to scientific data mining. IEEE Transactions on Knowledge and Data Engineering 14, 731–749 (2002)

    Article  Google Scholar 

  6. Yan, X., Han, J.: CloseGraph: mining closed frequent graph patterns. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, pp. 286–295. ACM, New York (2003)

    Chapter  Google Scholar 

  7. Garofalakis, M., Rastogi, R., Shim, K.: Mining sequential patterns with regular expression constraints. IEEE Transactions on Knowledge and Data Engineering 14, 530–552 (2002)

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Zaki, M.J.: SPADE: An efficient algoritm for mining frequent sequences. Machine Learning 42, 31–60 (2001)

    Article  MATH  Google Scholar 

  10. Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Hand, D., Keim, D., Ng, R. (eds.) Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, July 23-26. ACM, New York (2002)

    Google Scholar 

  11. Mannila, H., Toivonen, H.: Multiple uses of frequent sets and condensed representations. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 189–194. AAAI Press, Menlo Park (1996)

    Google Scholar 

  12. De Raedt, L.: A perspective on inductive databases. SIGKDD Explorations 4, 69–77 (2003)

    Article  Google Scholar 

  13. Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Communications of The ACM 39, 58–64 (1996)

    Article  Google Scholar 

  14. Mannila, H.: Inductive databases and condensed representations for data mining. In: Maluszynski, J. (ed.) Logic Programming, Proceedngs of the 1997 International Symposium, Port Jefferson, Long Island, N.Y., October 13-16, pp. 21–30. MIT Press, Cambridge (1997)

    Google Scholar 

  15. Gunopulos, D., Khardon, R., Mannila, H., Saluja, S., Toivonen, H., Sharma, R.S.: Discovering all most specific sentences. ACM Transactions on Database Systems 28, 140–174 (2003)

    Article  Google Scholar 

  16. Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  17. Boulicaut, J.F., Bykowski, A., Rigotti, C.: Free-sets: a condensed representation of Boolean data for the approximation of frequency queries. Data Mining and Knowledge Discovery 7, 5–22 (2003)

    Article  MathSciNet  Google Scholar 

  18. Bykowski, A., Rigotti, C.: A condensed representation to find frequent patterns. In: Proceedings of the Twenteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Santa Barbara, California, USA, May 21-23. ACM, New York (2001)

    Google Scholar 

  19. Kryszkiewicz, M.: Concise representation of frequent patterns based on disjunction-free generators. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, California, USA, November 29 - December 2, pp. 305–312. IEEE Computer Society, Los Alamitos (2001)

    Chapter  Google Scholar 

  20. Calders, T., Goethals, B.: Minimal k-free representations of frequent sets. In: [33], pp. 71–82

    Google Scholar 

  21. Calders, T., Goethals, B.: Mining all non-derivable frequent itemsets. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 74–85. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  22. Pei, J., Dong, G., Zou, W., Han, J.: On computing condensed pattern bases. In: [33], pp. 378–385

    Google Scholar 

  23. Mielikäinen, T., Mannila, H.: The pattern ordering problem. In: [33], pp. 327–338

    Google Scholar 

  24. Mielikäinen, T.: Chaining patterns. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds.) DS 2003. LNCS (LNAI), vol. 2843, pp. 233–244. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  25. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI/MIT Press (1996)

    Google Scholar 

  26. Hafez, A., Deogun, J., Raghavan, V.V.: The item-set tree: A data structure for data mining. In: Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 183–192. Springer, Heidelberg (1999)

    Google Scholar 

  27. Mielikäinen, T.: An automata approach to pattern collections. In: Goethals, B., Siebes, A. (eds.) KDID 2004. LNCS, vol. 3377, pp. 130–149. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  28. Mielikäinen, T.: Separating structure from interestingness. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 476–485. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  29. Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhai, L.: Mining frequent patterns with counting inference. SIGKDD Explorations 2, 66–75 (2000)

    Article  Google Scholar 

  30. Moffat, A., Neal, R.M., Witten, I.H.: Arithmetic coding revisited. ACM Transactions on Information Systems 16, 256–294 (1998)

    Article  Google Scholar 

  31. Pavlov, D., Mannila, H., Smyth, P.: Beyond independence: probabilistic methods for query approximation on binary transaction data. IEEE Transactions on Data and Knowledge Engineering 15, 1409–1421 (2003)

    Article  Google Scholar 

  32. Kumar, V., Tsumoto, S. (eds.): Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), Maebashi City, Japan, December 9-12. IEEE Computer Society, Los Alamitos (2002)

    Google Scholar 

  33. Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.): PKDD 2003. LNCS (LNAI), vol. 2838. Springer, Heidelberg (2003)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. HIIT Basic Research Unit, Department of Computer Science, University of Helsinki, Finland

    Taneli Mielikäinen

Authors
  1. Taneli Mielikäinen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Mathematics and computer Science Department, University of Antwerp, Middelheimlaan 1, 2020, Antwerp, Belgium

    Bart Goethals

  2. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mielikäinen, T. (2005). Implicit Enumeration of Patterns. In: Goethals, B., Siebes, A. (eds) Knowledge Discovery in Inductive Databases. KDID 2004. Lecture Notes in Computer Science, vol 3377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31841-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-31841-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25082-1

  • Online ISBN: 978-3-540-31841-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation