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International Conference on Information and Software Technologies

ICIST 2013: Information and Software Technologies pp 222–235Cite as

Modified Stochastic Algorithm for Mining Frequent Subsequences

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Part of the Communications in Computer and Information Science book series (CCIS,volume 403)

Abstract

The task of market basket analysis is one of the oldest areas of data mining, but still remains very relevant in today’s market. Supermarkets have enormous amounts of data about purchases and it is always important to know what items the market basket contains, how it fluctuates, whether it depends on a particular season, etc. In order to solve these tasks various data mining methods and algorithms are applied. One of them is discovering association rules. The article introduces the modified stochastic algorithm for mining frequent subsequences, as well as computer modeling results and conclusions are presented. The essence of the modified stochastic algorithm is to quickly discover frequent subsequences based on the 1-element subsequence discovered by the Apriori algorithm. In the algorithm the database is scanned once, frequent subsequences and association rules are discovered. The confidence of the algorithm is estimated applying probability statistical methods.

Keywords

  • frequent subsequence
  • association rule
  • Apriori algorithm
  • modified stochastic algorithm for mining frequent subsequences

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References

  1. Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential Pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 429–435. ACM Press, Edmonton (2002)

    CrossRef  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann, Santiago de Chile (1994)

    Google Scholar 

  3. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press, Washington, D.C. (1993)

    Google Scholar 

  4. Brin, S., Motwani, R., Silverstein, C.: Market Baskets: Generalizing Association Rules to Correlations. In: Proceedings ACM SIGMOD International Conference on Management of Data, pp. 265–276. ACM Press, Tucson (1997)

    Google Scholar 

  5. Cai-Yan, J., Xie-Ping, G.: Multi-scaling sampling: An adaptive sampling method for discovering approximate association rules. Journal of Computer Science and Technology 20, 309–318 (2005)

    CrossRef  MathSciNet  Google Scholar 

  6. Cheung, D.W., Han, J., Ng, V.T., Fu, A.W., Fu, Y.: A Fast Distributed Algorithm for Mining Association Rules. In: Proceedings of of the Fourth International Conference on Parallel and Distributed Information Systems, pp. 31–43. IEEE Computer Society, Miami Beach (1996)

    CrossRef  Google Scholar 

  7. Cho, C.-W., Wu, Y.-H., Chen, A.L.P.: Effective Database Transformation and Efficient Support Computation for Mining Sequential Patterns. In: Zhou, L.-Z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 163–174. Springer, Heidelberg (2005)

    Google Scholar 

  8. Coenen, F., Goulbourne, G., Leng, P.: Tree Structures for Mining Association Rules. In: Data Mining and Knowledge Discovery, vol. 8, pp. 25–51. Kluwer Academic Publishers (2004)

    Google Scholar 

  9. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. The MIT Press, Cambridge (2009)

    MATH  Google Scholar 

  10. Cekanavicius, V., Murauskas, G.: Statistika ir jos taikymai. TEV, Vilnius (2000)

    Google Scholar 

  11. Gharib, T.F., Nassar, H., Taha, M., Abraham, A.: An efficient algorithm for incremental mining of temporal association rules. In: Data & Knowledge Engineering, vol. 69, pp. 737–880. North-Holland (2010)

    Google Scholar 

  12. Gyenesei, A., Teuhola, J.: Probabilistic Iterative Expansion of Candidates in Mining Frequent Itemsets. In: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Melbourne, Florida, USA, pp. 192–195 (2003)

    Google Scholar 

  13. Huanyin, Z., Jinsheng, L.: The Research of A-Priori Algorithm Candidates Based on Support Counts. In: International Conference on Information Technology and Computer Science, pp. 192–195. TBD, Kiev (2009)

    Google Scholar 

  14. 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)

    CrossRef  Google Scholar 

  15. Pallavi, D.: Association Rule Mining on Distributed Data. International Journal of Scientific & Engineering Research 3, 1–6 (2012)

    Google Scholar 

  16. Rasoulian, M., Saeed, A.: The Effect of Data Mining Based on Association Rules in Strategic Management. Journal of Basic and Applied Scientific Research, 1742–1748 (2012)

    Google Scholar 

  17. Raorane, A.A., Kulkarni, R.V., Jitkar, B.D.: Association Rule – Extracting Knowledge Using Market Basket Analysis. Research Journal of Recent Sciences 1(2), 19–27 (2012)

    Google Scholar 

  18. Sandhu, P.S., Dhaliwal, D.S., Panda, S.N.: Mining utility-oriented association rules: An efficient approach based on profit and quantity. International Journal of the Physical Sciences 6(2), 301–307 (2011)

    Google Scholar 

  19. Savasere, A., Omiecinski, E., Navathe, S.: An Efficient Algorithm for Mining Association Rules in Large Databases. In: Proceedings of the 21st International Conference on Very Large Databases, Zurich, Swizerland, pp. 432–444 (1995)

    Google Scholar 

  20. Savulioniene, L., Sakalauskas, L.: Statistical algorithm for mining frequent sequences. Information Sciences 58, 126–143 (2011)

    Google Scholar 

  21. Savulioniene, L., Sakalauskas, L.: Stochastic algorithm for mining frequent sequences. Journal of Young Scientists 4(33), 138–145 (2011)

    Google Scholar 

  22. Siebes, A., Vreeken, J., Leeuwen, M.: Item Sets That Compress. In: Data Mining and Knowledge Discovery, vol. 23, pp. 169–214 (2011)

    Google Scholar 

  23. Srikant, R., Agrewal, R.: Mining generalized Association Rules. In: Proceeding VLDB 1995 Proceedings of the 21st International Conference on Very large Data Bases, San Francisco, CA, USA, pp. 407–419 (1995)

    Google Scholar 

  24. Toivonen, H.: Sampling Large Databases for Association Rules. In: Proceedings of the 22nd International Conference on Very Large Databases, Mumbai, India, pp. 134–145 (1996)

    Google Scholar 

  25. Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An efficient Algorithm for Incremental Updation of Association Rules in Large Database. In: Proceedings of 3rd International Conference on KDD and data mining (KDD 1997), Newport Beach, California, pp. 263–266 (2007)

    Google Scholar 

  26. Umarani, V., Punithavalli, M.: A study on effective mining of Association Rules from huge Databases. International Journal of Computer Science and Research 1, 30–34 (2010)

    Google Scholar 

  27. Wang, H., Liu, X.: The Research of Improved Association Rules Mining Apriori Algorithm. In: Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 961–964. IEEE, Shanghai (2011)

    Google Scholar 

  28. Yang, J., Zhao, C.: Study on the Data Mining Algorithm Based on Positive and Negative Association Rules. Computer and Information Science 2, 103–106 (2009)

    Google Scholar 

  29. Zaki, M.J.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. In: Machine Learning, vol. 42, pp. 31–60 (2001)

    Google Scholar 

  30. Zaki, M.J., Hsiao, C.: CHARM: An Efficient Algorithm for Closed Association Rule Mining. International Journal of Intelligent Systems Technologies and Applications 4, 313–326 (2008)

    CrossRef  Google Scholar 

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Authors and Affiliations

  1. Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania

    Loreta Savulioniene & Leonidas Sakalauskas

Authors
  1. Loreta Savulioniene
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  2. Leonidas Sakalauskas
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Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Studentu g. 50-313a, 51368, Kaunas, Lithuania

    Tomas Skersys

  2. Centre of Information Systems Design Technologies, Kaunas University of Technology, Studentu st. 50-313a, 51368, Kaunas, Lithuania

    Rimantas Butleris

  3. Kaunas University of Technology, Studentu g. 50-309a, 51368, Kaunas, Lithuania

    Rita Butkiene

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Savulioniene, L., Sakalauskas, L. (2013). Modified Stochastic Algorithm for Mining Frequent Subsequences. In: Skersys, T., Butleris, R., Butkiene, R. (eds) Information and Software Technologies. ICIST 2013. Communications in Computer and Information Science, vol 403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41947-8_19

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  • DOI: https://doi.org/10.1007/978-3-642-41947-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41946-1

  • Online ISBN: 978-3-642-41947-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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