Journal of Intelligent Information Systems

, Volume 47, Issue 1, pp 135–163 | Cite as

Evidential data mining: precise support and confidence

Article

Abstract

Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.

Keywords

Evidential database Evidential confidence Evidential support Associative classification 

Notes

Acknowledgments

The authors would like to express their sincere gratitude to the anonymous reviewers for their constructive and helpful comments and suggestions which have been in help to improve the quality of this paper.

References

  1. Aggarwal, C.C. (2009). Managing and mining uncertain data Vol. 35. Berlin Heidelberg New York: Springer.CrossRefMATHGoogle Scholar
  2. Aggarwal, C.C., Li, Y., Wang, J., & Wang, J. (2009). Frequent pattern mining with uncertain data. In Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, Paris, France (pp. 29–38).Google Scholar
  3. Agrawal, R., & Srikant, R. (1994). Fast algorithm for mining association rules. In Proceedings of international conference on very large databases, VLDB, Santiago de Chile, Chile (pp. 487–499).Google Scholar
  4. Bach Tobji, M.A., Ben Yaghlane, B., & Mellouli, K. (2009). Incremental maintenance of frequent itemsets in evidential databases. In Proceedings of the 10th European conference on symbolic and quantitative approaches to reasoning with uncertainty, Verona, Italy (pp. 457–468).Google Scholar
  5. Bell, D.A., Guan, J., & Lee, S.K. (1996). Generalized union and project operations for pooling uncertain and imprecise information. Data & Knowledge Engineering, 18(2), 89–117.CrossRefMATHGoogle Scholar
  6. Ben Yahia, S., Hamrouni, T., & Mephu Nguifo, E. (2006). Frequent closed itemset based algorithms: a thorough structural and analytical survey. SIGKDD Explorations, 8(1), 93–104.CrossRefGoogle Scholar
  7. Chui, C.K., Kao, B., & Hung, E. (2007). Mining frequent itemsets from uncertain data. In Proceedings of the 11th Pacific-Asia conference on advances in knowledge discovery and data mining, Nanjing, China (pp. 47–58).Google Scholar
  8. Dempster, A. (1967). Upper and lower probabilities induced by multivalued mapping. AMS-38.Google Scholar
  9. Dubois, D., & Prade, H. (1988). Possibility theory: an approach to computerized processing of uncertainty. New York: Plenum Press.CrossRefMATHGoogle Scholar
  10. Fagin, R., & Halpern, J.Y. (1990). A new approach to updating beliefs. In Proceedings of the 6th annual conference on uncertainty in artificial intelligence, UAI’90 (pp. 347–374). Amsterdam: Elsevier.Google Scholar
  11. Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml.
  12. Gärdenfors, P. (1983). Probabilistic reasoning and evidentiary value. In Evidentiary value: philosophical, judicial, and psychological aspects of a theory: essays dedicated to Sören Halldén on his 60th Birthday. C.W.K. Gleerups.Google Scholar
  13. Hewawasam, K.K.R., Premaratne, K., & Shyu, M.L. (2007). Rule mining and classification in a situation assessment application: a belief-theoretic approach for handling data imperfections. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 37 (6), 1446–1459.CrossRefGoogle Scholar
  14. Hewawasam, K.K.R., Premaratne, K., Shyu, M.L., & Subasingha, S.P. (2005). Rule mining and classification in the presence of feature level and class label ambiguities. In SPIE 5803, intelligent computing: theory and applications III, Vol. 98.Google Scholar
  15. Hong, T.P., Kuo, C.S., & Chi, S.C. (1999). Mining association rules from quantitative data. Intelligent Data Analysis, 3(5), 363–376.CrossRefMATHGoogle Scholar
  16. Hong, T.P., Kuo, C.S., & Wang, S.L. (2004). A fuzzy AprioriTid mining algorithm with reduced computational time. Applied Soft Computing, 5(1), 1–10.CrossRefGoogle Scholar
  17. Jousselme, A.L., & Maupin, P. (2012). Distance in evidence theory: comprehensive survey and generalizations. International Journal of Approximate Reasoning, 53(2), 118–145.MathSciNetCrossRefMATHGoogle Scholar
  18. Lee, S.K. (1992). An extended relational database model for uncertain and imprecise information. In Proceedings of the 18th international conference on very large data bases, VLDB92, Vancouver, British Columbia, Canada (pp. 211–220).Google Scholar
  19. Lee, S.K. (1992). Imprecise and uncertain information in databases: an evidential approach. In Proceedings of 8th international conference on data engineering, Tempe, AZ (pp. 614–621).Google Scholar
  20. Leung, C.K.S., Mateo, M.A.F., & Brajczuk, D.A. (2008). A tree-based approach for frequent pattern mining from uncertain data. In Proceedings of 12th Pacific-Asia conference on knowledge discovery and data mining, Osaka, Japan (vol. 5012 pp. 653–661).Google Scholar
  21. Li, W., Han, J., & Pei, J. (2001). CMAR: accurate and efficient classification based on multiple class-association rules. In Proceedings of IEEE international conference on data mining (ICDM01), San Jose, CA (pp. 369–376). IEEE Computer Society.Google Scholar
  22. Manjusha, R., & Ramachandran, R. (2011). Web mining framework for security in e-commerce. In Proceedings of international conference on recent trends in information technology (ICRTIT), Chennai, India (pp. 1043–1048).Google Scholar
  23. Masson, M.H., & Denœux, T. (2008). ECM: an evidential version of the fuzzy c-means algorithm. Pattern Recognition, 41(4), 1384–1397.CrossRefMATHGoogle Scholar
  24. Ordonez, C., Ezquerra, N., & Santana, C.A. (2006). Constraining and summarizing association rules in medical data. Knowledge and Information Systems, 9(3), 259–283.CrossRefGoogle Scholar
  25. Ordonez, C., & Omiecinski, E. (1999). Discovering association rules based on image content. In Proceedings of the IEEE advances in digital libraries conference (ADL’99), Baltimore, MD (pp. 38–49).Google Scholar
  26. Pasquier, N., Bastide, Y., Taouil, R., & Lakhal, L. (1999). Efficient mining of association rules using closed itemset lattices. Journal of Information Systems, 24, 25–46.CrossRefMATHGoogle Scholar
  27. Samet, A., Lefevre, E., & Ben Yahia, S. (2013). Mining frequent itemsets in evidential database. In Proceedings of the 5th international conference on knowledge and systems engeneering, Hanoi, Vietnam (pp. 377–388).Google Scholar
  28. Samet, A., Lefèvre, E., & Ben Yahia, S. (2014). Classification with evidential associative rules. In Proceedings of 15th international conference on information processing and management of uncertainty in knowledge-based systems, Montpellier, France (pp. 25–35).Google Scholar
  29. Samet, A., Lefevre, E., & Ben Yahia, S. (2014). Evidential database: a new generalization of databases? In Proceedings of 3rd international conference on belief functions, belief 2014, Oxford, UK (pp. 105–114).Google Scholar
  30. Smets, P. (1988). Belief functions. In P. Smets, A. Mamdani, D. Dubois, & H. Prade (Eds.), Non standard logics for automated reasoning (pp. 253–286). London: Academic.Google Scholar
  31. Smets, P. (1990). The transferable belief model and other interpretations of Dempster-Shafer’s model. In Proceedings of the 6th annual conference on uncertainty in artificial intelligence, UAI’90 (pp. 375–383). Cambridge: MIT.Google Scholar
  32. Smets, P., & Kennes, R. (1994). The transferable belief model. Artificial Intelligence, 66(2), 191–234.MathSciNetCrossRefMATHGoogle Scholar
  33. Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., & Lakhal, L. (2002). Computing iceberg concept lattices with titanic. Data & Knowledge Engineering, 42, 189–222.CrossRefMATHGoogle Scholar
  34. Tong, Y., Chen, L., Cheng, Y., & Yu, P.S. (2012). Mining frequent itemsets over uncertain databases. In Proceedings of the 38th International Conference on Very Large Databases, VLDB12, Istanbul, Turkey, 5(11), 1650–1661.Google Scholar
  35. Wu, X., Zhang, C., & Zhang, S. (2005). Database classification for multi-database mining. Information Systems, 30, 71–88.CrossRefMATHGoogle Scholar
  36. Yin, J., Zhou, X., & Yang, M. (2006). Data mining in incomplete database. Computer Engineering, 12, 013.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Université Lille Nord de France UArtoisBéthuneFrance
  2. 2.Faculty of Sciences of TunisUniversity of Tunis ElManar, LIPAH-LRTunisTunisia

Personalised recommendations