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ERAPN, an Algorithm for Extraction Positive and Negative Association Rules in Big Data

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Big Data Analytics and Knowledge Discovery (DaWaK 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11031))

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Abstract

Over the past two decades, the extraction of positive association rules is a significant research area in Big Data. While the negative association rules has been received a lot of attention, they remained in the shadows due to the difficulty of their extraction. In this paper, we propose Erapn, an algorithm for extraction of valid positive and negative association rules. Frequent patterns can be derived in a single pass to the database, because, of the new technique support counting, called reduction-access-database. As for the generation of potential valid association rules, we introduce a new technique, called reduction-space-rules, by dividing the space candidates into two. Only half of the candidates have to be studied through this technique. Some experiments will be leaded into such reference databases to complete our study.

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References

  1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD, pp. 207–216 (1993)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th VLDB Conference, Santiago, Chile, pp. 487–499 (1994)

    Google Scholar 

  3. Bemarisika, P., Totohasina, A.: Eomf, un algorithme d’extraction optimisée des motifs fréquents. In: Proceedings of AAFD & SFC, Marrakech, Maroc, pp. 198–203 (2016)

    Google Scholar 

  4. Bemarisika, P.: Extraction de règles d’association selon le couple support-\(M_{GK}\): Graphes implicatifs et Application en didactique des mathématiques. Université d’Antananarivo, Madagascar (2016)

    Google Scholar 

  5. Bemarisika, P., Totohasina, A.: Optimized mining of potential positive and negative association rules. In: Bellatreche, L., Chakravarthy, S. (eds.) DaWaK 2017. LNCS, vol. 10440, pp. 424–432. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64283-3_31

    Chapter  Google Scholar 

  6. Brin, S., Motwani, R., Silverstein, C.: Bayond market baskets: Generalizing association rules to correlation. In: Proceedings of the ACM SIGMOD, pp. 265–276 (1997)

    Google Scholar 

  7. Cornelis, C., Yan, P., Zhang, X., Chen, G.: Mining positive and negative association rules from large databases. In: Proceedings of the IEEE, pp. 613–618 (2006)

    Google Scholar 

  8. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-642-59830-2

    Book  MATH  Google Scholar 

  9. Guillaume, S.: Traitement de données volumineuses: Mesure et algorithmes d’extraction de règles d’association. Ph.D. thesis, Université de Nantes (2000)

    Google Scholar 

  10. Guillaume, S., Papon, P.-A.: Extraction optimisée de règles d’association positives et négatives (RAPN). In: Actes de la 13e Conf. Int., pp. 157–168. EGC, Franco (2013)

    Google Scholar 

  11. Savasere, A., Omiecinski, E., Navathe, S.: Mining for strong negative associations in a large database of customer transactions. In: Proceedings of ICDE, pp. 494–502 (1998)

    Google Scholar 

  12. Teng, W.-G., Ming-Jyh, H., Ming-Syan, C.: A statistical framework for mining substitution rules. Knowl. Inf. Syst. 7, 158–178 (2005)

    Article  Google Scholar 

  13. Totohasina, A., Ralambondrainy H.: ION, a pertinent new measure for mining information from many types of data. In: IEEE, SITIS, pp. 202–207 (2005)

    Google Scholar 

  14. Hämäläinen, W.: Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures. Knowl. Inf. Syst. 32, 383–414 (2012)

    Article  Google Scholar 

  15. Wu, X., Zhang, C., Zhang, S.: Efficient mining of both positive and negative association rules. ACM Trans. Inf. Syst. 3, 381–405 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratoire de Mathéiques et d’Informatique ENSET, Université d’Antsiranana, Antsiranana, Madagascar

    Parfait Bemarisika & André Totohasina

  2. Laboratoire d’Informatique et de Mathématiques EA2525, Université de La Réunion, Saint-Denis, France

    Parfait Bemarisika

Authors
  1. Parfait Bemarisika
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  2. André Totohasina
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Corresponding author

Correspondence to Parfait Bemarisika .

Editor information

Editors and Affiliations

  1. University of Houston, Houston, Texas, USA

    Carlos Ordonez

  2. LIAS/ISAE-ENSMA, Chasseneuil-du-Poitou, France

    Ladjel Bellatreche

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Bemarisika, P., Totohasina, A. (2018). ERAPN, an Algorithm for Extraction Positive and Negative Association Rules in Big Data. In: Ordonez, C., Bellatreche, L. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2018. Lecture Notes in Computer Science(), vol 11031. Springer, Cham. https://doi.org/10.1007/978-3-319-98539-8_25

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  • DOI: https://doi.org/10.1007/978-3-319-98539-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98538-1

  • Online ISBN: 978-3-319-98539-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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