Information Fusion and Machine Learning in Spatial Prediction for Local Agricultural Markets

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 887)


This research explores information fusion and data mining techniques and proposes a methodology to improve predictions based on strong associations among agricultural products, which allows prediction for future consumption in local markets in the Andean region of Ecuador using spatial prediction techniques. This commercial activity is performed using Alternative Marketing Circuits (CIALCO), seeking to establish a direct relationship between producer and consumer prices, and promote buying and selling among family groups.


Data fusion Alternative circuits of commercialization Associations mining Predictive analysis 



This work was supported in part by Project MINECO TEC2017-88048-C2-2-R and by Commercial Coordination Network, Ministry of Agriculture, Livestock, Aquaculture and Fisheries Ecuador.


  1. 1.
    Padilla, W.R., García, H.J.: CIALCO: Alternative marketing channels. Commun. Comput. Inf. Sci. 616, 313–321 (2016)Google Scholar
  2. 2.
    Zulfikar, W.B., Wahana, A., Uriawan, W., Lukman, N.: Implementation of association rules with apriori algorithm for increasing the quality of promotion. In: 2016 4th International Conference on Cyber and IT Service Management, pp. 1–5 (2016)Google Scholar
  3. 3.
    Association Rules. Accedido 12 dic 2017
  4. 4.
    Patil, S.D., Deshmukh, R.R., Kirange, D.K.: Adaptive apriori algorithm for frequent itemset mining. In: 2016 International Conference System Modeling Advancement in Research Trends (SMART), pp. 7–13 (2016)Google Scholar
  5. 5.
    Chang, C.-C., Li, Y.-C., Lee, J.-S.: An efficient algorithm for incremental mining of association rules. In: 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA 2005), pp. 3–10 (2005)Google Scholar
  6. 6.
    Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42, 31–60 (2001)Google Scholar
  7. 7.
  8. 8.
    Asadifar, S., Kahani, M.: Semantic association rule mining: a new approach for stock market prediction. In: 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), pp. 106–111 (2017)Google Scholar
  9. 9.
    Mane, R.V., Ghorpade, V.R.: Predicting student admission decisions by association rule mining with pattern growth approach. In: 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), pp. 202–207 (2016)Google Scholar
  10. 10.
    Kumar, P.S.V.V.S.R., Maddireddi, L.R.D.P., Anantha Lakshmi, V., Dirisala, J.N.K.: Novel fuzzy classification approaches based on optimisation of association rules. In: 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 1–5 (2016)Google Scholar
  11. 11.
  12. 12.
    Mitsa, T.: Temporal Data Mining. CRC Press (2010). Accedido 12 dic 2017
  13. 13.
    Won, K.S., Ray, T.: Performance of kriging and cokriging based surrogate models within the unified framework for surrogate assisted optimization. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), vol. 2, pp. 1577–1585 (2004)Google Scholar
  14. 14.
    Chen, D., Liu, D., Li, Y., Meng, L., Yang, X.: Improve spatiotemporal kriging with magnitude and direction information in variogram construction. Chin. J. Electron. 25(3), 527–532 (2016)Google Scholar
  15. 15.
    Celemín, J.P.: Autocorrelación espacial e indicadores locales de asociación espacial: Importancia, estructura y aplicación. Rev. Univ. Geogr. 18(1), 11–31 (20090Google Scholar
  16. 16.
    Weka 3 - Data Mining with Open Source Machine Learning Software in Java. [En línea]. Disponible en: Accedido 28 sep 2017
  17. 17.
    RStudio – Open source and enterprise-ready professional software for R. [En línea]. Disponible en: Accedido 08 dic 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research Group Ideia GeocaSalesian Polytechnic University of Quito-Ecuador Engineer SystemsQuitoEcuador
  2. 2.Applied Artificial Intelligence GroupCarlos III UniversityMadridSpain

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