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Scenario Query Based on Association Rules (SQAR)

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 610))

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Abstract

In the last years association rules are being applied to support decision making. However, the main concern is in the precision and not in the interpretability of their results, so they produce large sets of rules difficult to understand for the user. A comprehensible system should work according to the human decision making process, which is quite based on the case study and the scenario projection. Here we propose an association rule based system for scenario query (SQAR), where the user can perform “what if...?” queries, and get as response what usually happens under similar scenarios. Even more we enrich our proposal with a hierarchical structure that allows the definition of scenarios with different detail levels, to comply with the needs of the user.

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References

  1. Agrawal, R., Sritkant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of 20th International Conference on Very Large Data Bases, pp. 478–499 (1994)

    Google Scholar 

  2. Balcázar, J.L.: Redundancy, deduction schemes, and minimum-size bases for association rules. Logical Methods Comput. Sci. 6(2) (2010). http://arxiv.org/abs/1002.4286

  3. Balmin, A., Papadimitriou, T., Papakonstantinou, Y.: Hypothetical queries in an olap environment. In: VLDB, vol. 220, p. 231 (2000)

    Google Scholar 

  4. Conrado, C., de Oude, P.: Scenario-based reasoning and probabilistic models fordecision support. In: 2014 17th International Conference on Information Fusion (FUSION), pp. 1–9, July 2014

    Google Scholar 

  5. Georgopoulos, V., Chouliara, S., Stylios, C.: Fuzzy cognitive mapscenario-based medical decision support systems for education. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1813–1816, August 2014

    Google Scholar 

  6. Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., Baesens, B.: An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decis. Support Syst. 51(1), 141–154 (2011). http://www.sciencedirect.com/science/article/pii/S0167923610002368

    Article  Google Scholar 

  7. Lakshmanan, L.V., Russakovsky, A., Sashikanth, V.: What-if olap queries with changing dimensions. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 1334–1336. IEEE (2008)

    Google Scholar 

  8. Liao, S.H., Chu, P.H., Hsiao, P.Y.: Data mining techniques and applications: Adecade review from 2000 to 2011. Expert Syst. Appli. 39(12), 11303–11311 (2012). http://www.sciencedirect.com/science/article/pii/S0957417412003077

    Article  Google Scholar 

  9. Marín, N., Molina, C., Serrano, J.M., Vila, A.: A complexity guided algorithm for association rule extraction on fuzzy datacubes. IEEE Trans. Fuzzy Syst. 16, 693–714 (2008)

    Article  Google Scholar 

  10. Martin-Barragan, B., Lillo, R., Romo, J.: Interpretable support vector machines for functional data. Eur. J. Oper. Res. 232(1), 146–155 (2014). http://www.sciencedirect.com/science/article/pii/S0377221712006406

    Article  Google Scholar 

  11. Medeiros, S., Epelbaum, V., Moreira de Souza, J., Pimentel Esteves, M.: Fuzzy prospective scenarios in strategic planning in large-group decision. In: 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 43–48, June 2013

    Google Scholar 

  12. Meissner, P., Wulf, T.: Cognitive benefits of scenario planning: Its impact on biases and decision quality. Technol. Forecast. Soc. Chang. 80(4), 801–814 (2013). http://www.sciencedirect.com/science/article/pii/S0040162512002375 scenario Method: Current developments in theory and practice

    Article  Google Scholar 

  13. Molina, C., Rodriguez-Ariza, L., Sanchez, D., Vila, A.: A new fuzzy multidimensional model. IEEE Trans. Fuzzy Syst. 14(6), 897–912 (2006)

    Article  Google Scholar 

  14. Pang, J., Liu, L., Li, S.: A comparative study between “prediction-response” and “scenario-response” in unconventional emergency decision-making management. In: 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO), pp. 649–652, April 2011

    Google Scholar 

  15. Pendse, N., Creeth, R.: The Olap Report. Business Intelligence, London (1995)

    Google Scholar 

  16. Saxena, G., Narula, R., Mishra, M.: New dimension value introduction for in-memory what-if analysis. arXiv preprint arXiv:1302.0351 (2013)

  17. Shortliffe, E., Buchanan, B.: A model of inexact reasoning in medicine. Math. Biosci. 23(3), 351–379 (1975)

    Article  MathSciNet  Google Scholar 

  18. Solanki, S., Patel, J.: A survey on association rule mining. In: 2015 Fifth International Conference on Advanced Computing Communication Technologies (ACCT), pp. 212–216, February 2015

    Google Scholar 

  19. Stella, N., Peacock, J., Chuan, T.: Evaluating decisions made in common basketball game scenarios. In: 2012 Southeast Asian Network of Ergonomics Societies Conference (SEANES), pp. 1–5, July 2012

    Google Scholar 

  20. Wang, Z., Yu, Z., Jiang, Y., Hong, X.: Research on scenarios management fortesting decision-making behavior in decision simulation environment. In: 2012 International Conference on Systems and Informatics (ICSAI), pp. 1213–1217, May 2012

    Google Scholar 

  21. Zhengmeng, C., Haoxiang, J.: A brief review on decision support systems and it’s applications. In: 2011 International Symposium on IT in Medicine and Education (ITME), vol. 2, pp. 401–405 (2011)

    Google Scholar 

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Acknowledgements

This research is partially supported by projects Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (Fondo Europeo de Desarrollo Regional - FEDER) under project TIN2014-58227-P Descripción lingüística de información visual mediante técnicas de minería de datos y computación flexible and TIC1582 Mejora de la Accesibilidad a la información mediante el uso de contextos e interpretaciones adaptadas al usuario of the Consejeria de Economia, Innovacion, Ciencia y Empleo from Junta de Andalucia (Spain).

Author information

Authors and Affiliations

  1. Department of Computer Sciences, University of Jaen, Jaen, Spain

    Carlos Molina

  2. Department of Software Engineering, University of Granada, Granada, Spain

    Belen Prados-Suárez

  3. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Daniel Sanchez

Authors
  1. Carlos Molina
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  2. Belen Prados-Suárez
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  3. Daniel Sanchez
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Corresponding author

Correspondence to Carlos Molina .

Editor information

Editors and Affiliations

  1. INESC-ID,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Joao Paulo Carvalho

  2. LIP 6, Université Pierre et Marie Curie, Paris, France

    Marie-Jeanne Lesot

  3. School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Uzay Kaymak

  4. IDMEC,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Susana Vieira

  5. LIP6, Université Pierre et Marie Curie, CNRS, Paris, France

    Bernadette Bouchon-Meunier

  6. Machine Intelligence Institute, Iona College, New Rochelle, New York, USA

    Ronald R. Yager

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© 2016 Springer International Publishing Switzerland

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Molina, C., Prados-Suárez, B., Sanchez, D. (2016). Scenario Query Based on Association Rules (SQAR). In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-40596-4_45

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  • DOI: https://doi.org/10.1007/978-3-319-40596-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40595-7

  • Online ISBN: 978-3-319-40596-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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