POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES

Veronica Oliveira de Carvalho, Fabiano Fernandes dos Santos, Solange Oliveira Rezende

2011

Abstract

The post-processing of association rules is a difficult task, since a large number of patterns can be obtained. Many approaches have been developed to overcome this problem, as objective measures and clustering, which are respectively used to: (i) highlight the potentially interesting knowledge in domain; (ii) structure the domain, organizing the rules in groups that contain, somehow, similar knowledge. However, objective measures don’t reduce nor organize the collection of rules, making the understanding of the domain difficult. On the other hand, clustering doesn’t reduce the exploration space nor direct the user to find interesting knowledge, making the search for relevant knowledge not so easy. This work proposes the PAR-COM (Post-processing Association Rules with Clustering and Objective Measures) methodology that, combining clustering and objective measures, reduces the association rule exploration space directing the user to what is potentially interesting. Thereby, PAR-COM minimizes the user’s effort during the post-processing process.

References

  1. Aggelis, V. (2004). Association rules model of e-banking services. Data Mining V - Information and Communication Technologies, 5:46-55.
  2. Aggelis, V. (2004). Association rules model of e-banking services. Data Mining V - Information and Communication Technologies, 5:46-55.
  3. Baesens, B., Viaene, S., and Vanthienen, J. (2000). Postprocessing of association rules. In KDD'00: Proceedings of the Special Workshop on Post-processing, The 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 2-8.
  4. Baesens, B., Viaene, S., and Vanthienen, J. (2000). Postprocessing of association rules. In KDD'00: Proceedings of the Special Workshop on Post-processing, The 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 2-8.
  5. Changguo, Y., Nianzhong, W., Tailei, W., Qin, Z., and Xiaorong, Z. (2009). The research on the application of association rules mining algorithm in network intrusion detection. In Hu, Z. and Liu, Q., editors, ETCS'09: Proceedings of the 1st International Workshop on Education Technology and Computer Science, volume 2, pages 849-852.
  6. Changguo, Y., Nianzhong, W., Tailei, W., Qin, Z., and Xiaorong, Z. (2009). The research on the application of association rules mining algorithm in network intrusion detection. In Hu, Z. and Liu, Q., editors, ETCS'09: Proceedings of the 1st International Workshop on Education Technology and Computer Science, volume 2, pages 849-852.
  7. Domingues, M. A., Jorge, A. M., and Soares, C. (2006). Using association rules for monitoring meta-data quality in web portals. In WAAMD'06: Proceedings of the II Workshop em Algoritmos e Aplicac¸ o˜es de Minerac¸a˜o de Dados - SBBD/SBES, pages 105-108.
  8. Domingues, M. A., Jorge, A. M., and Soares, C. (2006). Using association rules for monitoring meta-data quality in web portals. In WAAMD'06: Proceedings of the II Workshop em Algoritmos e Aplicac¸ o˜es de Minerac¸a˜o de Dados - SBBD/SBES, pages 105-108.
  9. Fonseca, B. M., Golgher, P. B., Moura, E. S., and Ziviani, N. (2003). Using association rules to discover search engines related queries. In LA-WEB'03: Proceedings of the 1st Conference on Latin American Web Congress, pages 66-71. IEEE Computer Society.
  10. Fonseca, B. M., Golgher, P. B., Moura, E. S., and Ziviani, N. (2003). Using association rules to discover search engines related queries. In LA-WEB'03: Proceedings of the 1st Conference on Latin American Web Congress, pages 66-71. IEEE Computer Society.
  11. Frank, A. and Asuncion, A. (2010). UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http://archive.ics.uci.edu/ml.
  12. Frank, A. and Asuncion, A. (2010). UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http://archive.ics.uci.edu/ml.
  13. Geng, L. and Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. In ACM Computing Surveys, volume 38. ACM Press.
  14. Geng, L. and Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. In ACM Computing Surveys, volume 38. ACM Press.
  15. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, second edition. http://wwwstat.stanford.edu/ tibs/ElemStatLearn/.
  16. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, second edition. http://wwwstat.stanford.edu/ tibs/ElemStatLearn/.
  17. Jorge, A. (2004). Hierarchical clustering for thematic browsing and summarization of large sets of association rules. In Berry, M. W., Dayal, U., Kamath, C., and Skillicorn, D., editors, SIAM'04: Proceedings of the 4th SIAM International Conference on Data Mining. 10p.
  18. Jorge, A. (2004). Hierarchical clustering for thematic browsing and summarization of large sets of association rules. In Berry, M. W., Dayal, U., Kamath, C., and Skillicorn, D., editors, SIAM'04: Proceedings of the 4th SIAM International Conference on Data Mining. 10p.
  19. Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. WileyInterscience.
  20. Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. WileyInterscience.
  21. Metwally, A., Agrawal, D., and Abbadi, A. E. (2005). Using association rules for fraud detection in web advertising networks. In VLDB'05: Proceedings of the 31st International Conference on Very Large Data Bases, pages 169-180.
  22. Metwally, A., Agrawal, D., and Abbadi, A. E. (2005). Using association rules for fraud detection in web advertising networks. In VLDB'05: Proceedings of the 31st International Conference on Very Large Data Bases, pages 169-180.
  23. Natarajan, R. and Shekar, B. (2005). Interestingness of association rules in data mining: Issues relevant to ecommerce. SA¯DHANA¯ - Academy Proceedings in Engineering Sciences (The Indian Academy of Sciences), 30(Parts 2&3):291-310.
  24. Natarajan, R. and Shekar, B. (2005). Interestingness of association rules in data mining: Issues relevant to ecommerce. SA¯DHANA¯ - Academy Proceedings in Engineering Sciences (The Indian Academy of Sciences), 30(Parts 2&3):291-310.
  25. Ohsaki, M., Kitaguchi, S., Okamoto, K., Yokoi, H., and Yamaguchi, T. (2004). Evaluation of rule interestingness measures with a clinical dataset on hepatitis. In Boulicaut, J.-F., Esposito, F., Giannotti, F., and Pedreschi, D., editors, PKDD'04: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, volume 3202, pages 362-373. Springer-Verlag New York, Inc.
  26. Ohsaki, M., Kitaguchi, S., Okamoto, K., Yokoi, H., and Yamaguchi, T. (2004). Evaluation of rule interestingness measures with a clinical dataset on hepatitis. In Boulicaut, J.-F., Esposito, F., Giannotti, F., and Pedreschi, D., editors, PKDD'04: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, volume 3202, pages 362-373. Springer-Verlag New York, Inc.
  27. Rajasekar, U. and Weng, Q. (2009). Application of association rule mining for exploring the relationship between urban land surface temperature and biophysical/social parameters. Photogrammetric Engineering & Remote Sensing, 75(3):385-396.
  28. Rajasekar, U. and Weng, Q. (2009). Application of association rule mining for exploring the relationship between urban land surface temperature and biophysical/social parameters. Photogrammetric Engineering & Remote Sensing, 75(3):385-396.
  29. Reynolds, A. P., Richards, G., de la Iglesia, B., and Rayward-Smith, V. J. (2006). Clustering rules: A comparison of partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms, 5(4):475-504.
  30. Reynolds, A. P., Richards, G., de la Iglesia, B., and Rayward-Smith, V. J. (2006). Clustering rules: A comparison of partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms, 5(4):475-504.
  31. Sahar, S. (2002). Exploring interestingness through clustering: A framework. In ICDM'02: Proceedings of the IEEE International Conference on Data Mining, pages 677-680.
  32. Sahar, S. (2002). Exploring interestingness through clustering: A framework. In ICDM'02: Proceedings of the IEEE International Conference on Data Mining, pages 677-680.
  33. Semenova, T., Hegland, M., Graco, W., and Williams, G. (2001). Effectiveness of mining association rules for identifying trends in large health databases. In Kurfess, F. J. and Hilario, M., editors, ICDM'01: Workshop on Integrating Data Mining and Knowledge Management, The IEEE International Conference on Data Mining. 12p.
  34. Semenova, T., Hegland, M., Graco, W., and Williams, G. (2001). Effectiveness of mining association rules for identifying trends in large health databases. In Kurfess, F. J. and Hilario, M., editors, ICDM'01: Workshop on Integrating Data Mining and Knowledge Management, The IEEE International Conference on Data Mining. 12p.
  35. Tan, P.-N., Kumar, V., and Srivastava, J. (2004). Selecting the right objective measure for association analysis. Information Systems, 29(4):293-313.
  36. Tan, P.-N., Kumar, V., and Srivastava, J. (2004). Selecting the right objective measure for association analysis. Information Systems, 29(4):293-313.
  37. Toivonen, H., Klemettinen, M., Ronkainen, P., Hätönen, K., and Mannila, H. (1995). Pruning and grouping discovered association rules. Workshop Notes of the ECML'95 Workshop on Statistics, Machine Learning, and Knowledge Discovery in Databases, 47-52, MLnet.
  38. Toivonen, H., Klemettinen, M., Ronkainen, P., Hätönen, K., and Mannila, H. (1995). Pruning and grouping discovered association rules. Workshop Notes of the ECML'95 Workshop on Statistics, Machine Learning, and Knowledge Discovery in Databases, 47-52, MLnet.
  39. Zhang, J. and Gao, W. (2008). Application of association rules mining in the system of university teaching appraisal. In ETTANDGRS'08: Proceedings of the International Workshop on Education Technology and Training & International Workshop on Geoscience and Remote Sensing, volume 2, pages 26-28. IEEE Computer Society.
  40. Zhang, J. and Gao, W. (2008). Application of association rules mining in the system of university teaching appraisal. In ETTANDGRS'08: Proceedings of the International Workshop on Education Technology and Training & International Workshop on Geoscience and Remote Sensing, volume 2, pages 26-28. IEEE Computer Society.
  41. Zhao, Y., Zhang, C., and Cao, L. (2009). Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction. Information Science Reference. 372p.
  42. Zhao, Y., Zhang, C., and Cao, L. (2009). Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction. Information Science Reference. 372p.
Download


Paper Citation


in Harvard Style

Carvalho V., Santos F. and Rezende S. (2011). POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-53-9, pages 54-63. DOI: 10.5220/0003457500540063


in Harvard Style

Carvalho V., Santos F. and Rezende S. (2011). POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-53-9, pages 54-63. DOI: 10.5220/0003457500540063


in Bibtex Style

@conference{iceis11,
author={Veronica Oliveira de Carvalho and Fabiano Fernandes dos Santos and Solange Oliveira Rezende},
title={POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2011},
pages={54-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003457500540063},
isbn={978-989-8425-53-9},
}


in Bibtex Style

@conference{iceis11,
author={Veronica Oliveira de Carvalho and Fabiano Fernandes dos Santos and Solange Oliveira Rezende},
title={POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2011},
pages={54-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003457500540063},
isbn={978-989-8425-53-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES
SN - 978-989-8425-53-9
AU - Carvalho V.
AU - Santos F.
AU - Rezende S.
PY - 2011
SP - 54
EP - 63
DO - 10.5220/0003457500540063


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES
SN - 978-989-8425-53-9
AU - Carvalho V.
AU - Santos F.
AU - Rezende S.
PY - 2011
SP - 54
EP - 63
DO - 10.5220/0003457500540063