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A Recursive Algorithm for Mining Association Rules

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Abstract

Mining frequent itemsets and association rules are an essential task within data mining and data analysis. In this paper, we introduce PrefRec, a recursive algorithm for finding frequent itemsets and association rules. Its main advantage is its recursiveness with respect to the items. It is particularly efficient for updating the mining process when new items are added to the database or when some are excluded. We present in a complete way the logic of the algorithm, and give some of its applications. After that, we carry out an experimental study on the effectiveness of PrefRec. We first compare the execution times with some very popular frequent itemset mining algorithms. Then, we do experiments to test the updating capabilities of our algorithm.

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References

  1. Agrawal R, Shafer J. Parallel mining of association rules: design, implementation, and experience. IEEE Trans Knowl Data Eng. 1996;8:962–9.

    Article  Google Scholar 

  2. Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proceedings of the 1994 international conference on very large data bases (VLDB 94) (Santiago, Chile); 1994. pp. 487–499.

  3. Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proceedings of the 1993ACM-SIGMOD international conference on management of data (SIGMOD’93) (Washington, DC); 1993. pp. 207–216.

  4. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo A. Fast discovery of association rules. In: Advances in knowledge discovery and data mining, AAAI/MIT Press; 1996. pp. 307–328.

  5. Borgelt C. Frequent item set mining WIREs. Data Mining Knowl Discov. 2012;2:437–56.

    Article  Google Scholar 

  6. Brin S, Motwani R, Ullman JD, Tsur S. Dynamic itemset counting and implication rules for market basket analysis. In: Proceeding of the 1997 ACM-SIGMOD international conference on management of data (SIGMOD’97) (Tucson, AZ); 1997. pp. 255–264. https://doi.org/10.1145/253260.253325.

  7. Cheung DW, Han J, Ng V, Fu A, Fu Y. A fast distributed algorithm for mining association rules. In: Proceeding of the 1996 international conference on parallel and distributed information systems (Miami Beach, FL); 1996. pp. 31–42.

  8. Cheung DW, Han J, Ng V, Wong CY. Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceeding of the 1996 international conference on data engineering (ICDE’96) (New Orleans, LA); 1996. pp. 106–114.

  9. Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In: Proceeding of the 2000 ACM-SIGMOD international conference on management of data (SIGMOD’00) (Dallas, TX); 2000. pp. 1–12.

  10. Han J, Cheng H, **n D, Yan X. Frequent pattern mining: current status and future directions. Data Min Knowl Disc. 2007;15:55–86.

    Article  MathSciNet  Google Scholar 

  11. Huang L, Chen H, Wang X, Chen G. A fast algorithm for mining association rules. Comput Sci Technol. 2000;15:619–24.

    Article  Google Scholar 

  12. Luna JM, Fournier-Viger P, Ventura S. Frequent itemset mining. Frequent itemset mining: a 25 Years Review; 2019. https://doi.org/10.1002/widm.1329.

  13. Park JS, Chen MS, Yu PS. An effective hash-based algorithm for mining association rules. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data (San Jose, California, USA); 1995. pp. 175–186. https://doi.org/10.1145/568271.223813.

  14. Sarawagi S, Thomas S, Agrawal R. Integrating association rule mining with relational database systems: alternatives and implications. In: Proceeding of the 1998 ACM-SIGMOD international conference on management of data (SIGMOD’98) (Seattle, WA); 1998. pp. 343–354.

  15. Savasere A, Omiecinski E, Navathe S. An efficient algorithm for mining association rules in large databases. In: Proceeding of the 1995 international conference on very large data bases (VLDB’95) (Zurich, Switzerland); 1995. pp. 432–444.

  16. Toivonen H. Sampling large databases for association rules. In: Proceeding of the 1996 international conference on very large data bases (VLDB’96) (Bombay, India); 1996. pp. 134–145.

  17. Uno T, Asai T, Uchida Y, Arimura H. LCM: an efficient algorithm for enumerating frequent closed item sets. In: Proceedings of the workshop on frequent item set mining implementations (FIMI 2003) (Melbourne,FL). TU Aachen, Germany: CEUR Workshop Proceedings 90; 2003.

  18. Zaki MJ. Scalable algorithms for association mining. IEEE Trans Knowl Data Eng. 2000;12:372–90.

    Article  Google Scholar 

  19. Zaki MJ, Parthasarathy S, Ogihara M, Li W. Parallel algorithm for discovery of association rules. Data Mining Knowl Discov. 1997;1:343–73.

    Article  Google Scholar 

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Acknowledgements

We would like to thank the Maisons du Monde data team for the numerous discussions we had on the various applications of the PrefRec algorithm.

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Authors and Affiliations

  1. Laboratoire de Mathématiques de Versailles, Université Paris-Saclay, UVSQ, CNRS, 78000, Versailles, France

    Abdelkader Mokkadem, Mariane Pelletier & Louis Raimbault

  2. Maisons du Monde France SAS, Le Portereau, 44120, Vertou, France

    Louis Raimbault

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  1. Abdelkader Mokkadem
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  2. Mariane Pelletier
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  3. Louis Raimbault
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Correspondence to Abdelkader Mokkadem.

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Mokkadem, A., Pelletier, M. & Raimbault, L. A Recursive Algorithm for Mining Association Rules. SN COMPUT. SCI. 3, 384 (2022). https://doi.org/10.1007/s42979-022-01266-y

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