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A Relational View of Pattern Discovery

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Database Systems for Advanced Applications (DASFAA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6587))

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Abstract

The elegant integration of pattern mining techniques into database remains an open issue. In particular, no language is able to manipulate data and patterns without introducing opaque operators or loop-like statement. In this paper, we cope with this problem using relational algebra to formulate pattern mining queries. We introduce several operators based on the notion of cover allowing to express a wide range of queries like the mining of frequent patterns. Beyond modeling aspects, we show how to reason on queries for characterizing and rewriting them for optimization purpose. Thus, we algebraically reformulate the principle of the levelwise algorithm.

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References

  1. Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley, Reading (1995)

    MATH  Google Scholar 

  2. Agrawal, R., Mehta, M., Shafer, J.C., Srikant, R., Arning, A., Bollinger, T.: The quest data mining system. In: KDD, pp. 244–249 (1996)

    Google Scholar 

  3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB, pp. 487–499. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  4. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Yu, P.S., Chen, A.L.P. (eds.) ICDE, pp. 3–14. IEEE Computer Society, Los Alamitos (1995)

    Google Scholar 

  5. Arimura, H., Uno, T.: Polynomial-delay and polynomial-space algorithms for mining closed sequences, graphs, and pictures in accessible set systems. In: SDM, pp. 1087–1098. SIAM, Philadelphia (2009)

    Google Scholar 

  6. Blockeel, H., Calders, T., Fromont, É., Goethals, B., Prado, A., Robardet, C.: An inductive database prototype based on virtual mining views. In: KDD, pp. 1061–1064. ACM, New York (2008)

    Chapter  Google Scholar 

  7. Bonchi, F., Giannotti, F., Lucchese, C., Orlando, S., Perego, R., Trasarti, R.: ConQueSt: a constraint-based querying system for exploratory pattern discovery. In: ICDE, p. 159. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  8. Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430. IEEE Computer Society, Los Alamitos (2001)

    Google Scholar 

  9. Boulicaut, J.F., Jeudy, B.: Constraint-based data mining. In: Maimon, O., Rokach, L. (eds.) The Data Mining and Knowledge Discovery Handbook, pp. 399–416. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. Calders, T., Lakshmanan, L.V.S., Ng, R.T., Paredaens, J.: Expressive power of an algebra for data mining. ACM Trans. Database Syst. 31(4), 1169–1214 (2006)

    Article  Google Scholar 

  11. Chomicki, J.: Querying with intrinsic preferences. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Hwang, J., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 34–51. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Crémilleux, B., Soulet, A.: Discovering knowledge from local patterns with global constraints. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part II. LNCS, vol. 5073, pp. 1242–1257. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  13. Diop, C.T., Giacometti, A., Laurent, D., Spyratos, N.: Composition of mining contexts for efficient extraction of association rules. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Hwang, J., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 106–123. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  14. Dzeroski, S.: Towards a general framework for data mining. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 259–300. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  15. Fu, A.W.C., Kwong, R.W., Tang, J.: Mining n-most interesting itemsets. In: Ohsuga, S., Raś, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 59–67. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  16. Han, J., Fu, Y., Wang, W., Chiang, J., Gong, W., Koperski, K., Li, D., Lu, Y., Rajan, A., Stefanovic, N., Xia, B., Zaïane, O.R.: DBMiner: a system for mining knowledge in large relational databases. In: KDD, pp. 250–255 (1996)

    Google Scholar 

  17. Hand, D.J.: Pattern detection and discovery. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 1–12. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  18. Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Commun. ACM 39(11), 58–64 (1996)

    Article  Google Scholar 

  19. Imielinski, T., Virmani, A.: MSQL: a query language for database mining. Data Min. Knowl. Discov. 3(4), 373–408 (1999)

    Article  Google Scholar 

  20. Johnson, T., Lakshmanan, L.V.S., Ng, R.T.: The 3W model and algebra for unified data mining. In: Abbadi, A.E., Brodie, M.L., Chakravarthy, S., Dayal, U., Kamel, N., Schlageter, G., Whang, K.Y. (eds.) VLDB, pp. 21–32. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  21. Khiari, M., Boizumault, P., Crémilleux, B.: Combining CSP and constraint-based mining for pattern discovery. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds.) ICCSA 2010. LNCS, vol. 6017, pp. 432–447. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  22. Li, C., Chang, K.C.C., Ilyas, I.F., Song, S.: RankSQL: query algebra and optimization for relational top-k queries. In: Özcan, F. (ed.) SIGMOD Conference, pp. 131–142. ACM Press, New York (2005)

    Google Scholar 

  23. Liu, H.C., Ghose, A., Zeleznikow, J.: Towards an algebraic framework for querying inductive databases. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5982, pp. 306–312. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  24. Mannila, H.: Theoretical frameworks for data mining. SIGKDD Explorations 1(2), 30–32 (2000)

    Article  Google Scholar 

  25. Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Min. Knowl. Discov. 1(3), 241–258 (1997)

    Article  Google Scholar 

  26. Meo, R., Psaila, G., Ceri, S.: A new SQL-like operator for mining association rules. In: Vijayaraman, T.M., Buchmann, A.P., Mohan, C., Sarda, N.L. (eds.) VLDB, pp. 122–133. Morgan Kaufmann, San Francisco (1996)

    Google Scholar 

  27. Mitchell, T.M.: Generalization as search. Artif. Intell. 18(2), 203–226 (1982)

    Article  MathSciNet  Google Scholar 

  28. Nijssen, S., Raedt, L.D.: IQL: a proposal for an inductive query language. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 189–207. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  29. Raedt, L.D.: A logical database mining query language. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 78–92. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  30. Raedt, L.D., Guns, T., Nijssen, S.: Constraint programming for itemset mining. In: KDD, pp. 204–212. ACM, New York (2008)

    Chapter  Google Scholar 

  31. Romei, A., Turini, F.: Inductive database languages: requirements and examples. Knowledge and Information Systems 1–34 (2010), http://dx.doi.org/10.1007/s10115-009-0281-4

  32. Terrovitis, M., Vassiliadis, P., Skiadopoulos, S., Bertino, E., Catania, B., Maddalena, A., Rizzi, S.: Modeling and language support for the management of pattern-bases. Data Knowl. Eng. 62(2), 368–397 (2007)

    Article  Google Scholar 

  33. Wang, H., Zaniolo, C.: ATLaS: a native extension of SQL for data mining. In: Barbará, D., Kamath, C. (eds.) SDM. SIAM, Philadelphia (2003)

    Google Scholar 

  34. Wicker, J., Richter, L., Kessler, K., Kramer, S.: SINDBAD and SiQL: an inductive database and query language in the relational model. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 690–694. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Université François Rabelais Tours, LI 3 place Jean Jaurès, F-41029, Blois, France

    Arnaud Giacometti, Patrick Marcel & Arnaud Soulet

Authors
  1. Arnaud Giacometti
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  2. Patrick Marcel
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  3. Arnaud Soulet
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Editor information

Editors and Affiliations

  1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong,, Shatin, N.T., Hong Kong, China

    Jeffrey Xu Yu

  2. Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro (373-1 Guseong-don), Yuseong-gu, 305-701, Daejeon, Korea

    Myoung Ho Kim

  3. Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Schützenbahn 70, 45117, Essen, Germany

    Rainer Unland

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Giacometti, A., Marcel, P., Soulet, A. (2011). A Relational View of Pattern Discovery. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20149-3_13

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  • DOI: https://doi.org/10.1007/978-3-642-20149-3_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20148-6

  • Online ISBN: 978-3-642-20149-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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