Abstract
In this paper, we are interested of the end-user for who have been defined different approaches for Knowledge Discovery in Database (KDD). One of the problems met with these approaches is the big number of generated rules that are not easily assimilated by the human brain. In this paper, we discuss these problems and we propose a pragmatic solution by (1) proposing a new approach for KDD through the fusion of conceptual clustering, fuzzy logic and formal concept analysis, and by (2) defining an Expert System (ES) allowing the user to easily exploit all generated knowledge in the first step. Indeed, this ES can help the user to give semantics of data and to optimize the research of information. This solution is extensible; the user can choose the fuzzy method of classification according to the domain of his data and his needs or the Inference Engine for the ES.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Goebel, M., Gruenwald, L.: A Survey of Data Mining and Knowledge Discovery Software Tools. ACM SIGKDD 1(1), 20–33 (1999)
Zaki, M.: Mining Non-Redundant Association Rules. Data Mining and Knowledge Discovery 9, 223–248 (2004)
Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Intelligent structuring and reducing of association rules with formal concept analysis. In: Baader, F., Brewka, G., Eiter, T. (eds.) KI 2001. LNCS (LNAI), vol. 2174, pp. 335–350. Springer, Heidelberg (2001)
Pasquier, N.: Data Mining: Algorithmes d’Extraction et de Réduction des Règles d’Association dans les Bases de Données. Thèse, Département d’Informatique et Statistique, Faculté des Sciences Economiques et de Gestion, Lyon (2000)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between sets of items in large Databases. In: Proceedings of the ACM SIGMOD Intl. Conference on Management of Data, Washington, USA, June 1993, pp. 207–216 (1993)
Agrawal, R., Skirant, R.: Fast algoritms for mining association rules. In: Proceedings of the 20th Int’l. Conference on Very Large Databases, June 1994, pp. 478–499 (1994)
Thanh, T.T., Hui, S.C., Cao, T.H.: A Fuzzy FCA-based Approach to Conceptual Clustering for Automatic Generation of Concept Hierarchy on Uncertainity Data. In: CLA, pp. 1–12 (2004)
Grissa Touzi, A., Sassi, M., Ounelli, H.: An innovative contribution to flexible query through the fusion of conceptual clustering, fuzzy logic, and formal concept analysis. International Journal of Computers and Their Applications 16(4), 220–233 (2009)
Sun, H., Wanga, S., Jiangb, Q.: FCM-Based Model Selection Algorithms for Determining the Number of Clusters. Pattern Recognition 37, 2027–2037 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grissa Touzi, A. (2010). About the End-User for Discovering Knowledge. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_76
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)