Towards Association Rules as a Predictive Tool for Geospatial Areas Evolution

Asma Gharbi, Cyril De Runz, Sami Faiz, Herman Akdag

2016

Abstract

Although it was basically presented as an exploratory tool rather than a predictive tool, numerous follow up researches have enhanced association rule mining, which contributes in making it a powerful predictive tool. In this context, this paper review the main advances in this datamining technique, then attempts to describe how they can, practically, be harnessed to deal with problems such as the prediction of geographical areas evolution.

References

  1. Aggarwal, C. C., Bhuiyan, M. A., and Hasan, M. A. (2014). Frequent pattern mining algorithms: A survey. In Aggarwal, C. C. and Han, J., editors, Frequent Pattern Mining, pages 19-64. Springer International Publishing.
  2. Agrawal, R., ImieliÁski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. In ACM SIGMOD Record, volume 22, pages 207-216. ACM.
  3. Chen, J., Miller, C., and Dagher, G. (2014). Product recommendation system for small online retailers using association rules mining. In Innovative Design and Manufacturing (ICIDM), Proceedings of the 2014 International Conference on, pages 71-77.
  4. Farzanyar, Z. and Kangavari, M. R. (2012). Efficient mining of fuzzy association rules from the pre-processed dataset. Computing and Informatics, 31(2):331-347.
  5. Gharbi, A., de Runz, C., Faiz, S., and Akdag, H. (2014). An association rules based approach to predict semantic land use evolution in the french city of saint-denis. International Journal of Data Warehousing and Mining, 10:1-17.
  6. Ilayaraja, M. and Meyyappan, T. (2015). Efficient data mining method to predict the risk of heart diseases through frequent itemsets. Procedia Computer Science, 70:586 - 592. Proceedings of the 4th International Conference on Eco-friendly Computing and Communication Systems.
  7. Lin, J. and Li, X. (2015). Knowledge transfer for largescale urban growth modeling based on formal concept analysis. Transactions in GIS, pages n/a-n/a.
  8. Park, H. A., Kim, T., Li, M., Shon, H. S., Park, J. S., and Ryu, K. H. (2015). Application of gap-constraints given sequential frequent pattern mining for protein function prediction. Osong Public Health and Research Perspectives, 6(2):112 - 120.
  9. Rudin, C., Letham, B., and Madigan, D. (2013). Learning theory analysis for association rules and sequential event prediction. Journal of Machine Learning Research, 14:3441-3492.
  10. Ryan, C. and Brown, K. (2013). Predicting occupant locations using association rule mining. In Bramer, M. and Petridis, M., editors, Research and Development in Intelligent Systems XXX, pages 63-77. Springer International Publishing.
  11. Thabtah, F., Cowling, P., and Peng, Y. (2004). Mmac: a new multi-class, multi-label associative classification approach. In Data Mining, 2004. ICDM 7804. Fourth IEEE International Conference on, pages 217-224.
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Paper Citation


in Harvard Style

Gharbi A., De Runz C., Faiz S. and Akdag H. (2016). Towards Association Rules as a Predictive Tool for Geospatial Areas Evolution . In Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-188-5, pages 201-206. DOI: 10.5220/0005914202010206


in Bibtex Style

@conference{gistam16,
author={Asma Gharbi and Cyril De Runz and Sami Faiz and Herman Akdag},
title={Towards Association Rules as a Predictive Tool for Geospatial Areas Evolution},
booktitle={Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2016},
pages={201-206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005914202010206},
isbn={978-989-758-188-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Towards Association Rules as a Predictive Tool for Geospatial Areas Evolution
SN - 978-989-758-188-5
AU - Gharbi A.
AU - De Runz C.
AU - Faiz S.
AU - Akdag H.
PY - 2016
SP - 201
EP - 206
DO - 10.5220/0005914202010206