Abstract
Rule mining is very important for data mining. However, traditional association rule is relatively weak in semantic representation. To address it, the main contributions of this paper included: (1) proposing formal concepts on h-Dimensional Enhanced Semantic Association Rule (h-DESAR) with self-contained logic operator; (2) proposing the h-DESAR mining method based on Immune-based Gene Expression Programming (ERIG); (3) presenting some novel key techniques in ERIG. Experimental results showed that ERIG is feasible, effective and stable.
This paper was supported by the National Science Foundation of China under Grant Nos. 60473071 and 90409007.
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
Agrawal, R., Imiclinski, T., Swami, A.: Database mining: A performance perspective. IEEE Trans. Knowledge and Data Enginnering 5, 914–925 (1993)
Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: Proceeding 1994 International conference Very Large Data Bases (VLDB 1994) (1994)
Han, J., Kambr, M.: Data Mining-Concepts and Techniques. Higher Education Press, Beijing (2001)
Fu, Y., Han, J.: Meta-rule-guided mining of association rules in relational databases. In: KDOOD 1995, Singapore, pp. 39–46 (December 1995)
Zuo, J., Tang, C., Tianqing, Z.: Mining Predicate Association Rule by Gene Expression Programming. In: Meng, X., Su, J., Wang, Y. (eds.) WAIM 2002. LNCS, vol. 2419, p. 92. Springer, Heidelberg (2002)
Ferreira, C.: Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13(2), 87–129 (2001)
Zuo, J.: Research on the Key Techniques of Gene Expression Programming: [Ph. D. dissertation]. Sichuan University, Sichuan (2004)
Silberschatz, K.: Databse System Concepts, 4th edn. McGraw-Hill Computer Science Series (2001)
De Castro, L.N., Von Zuben, F.J.: Artificial Immune Systems: Part I-Basic Theory and Applications. Technical Report, TR- DCA Ol/99, p. 12 (December 1999)
Dasgupta, D., Ji, Z., Gonzalez, F.: Artificial immune system (AIS) research in the last five years. In: Evolutionary Computation, CEC 2003 (2003)
Forrest, S., Perelson., A.S., et al.: Self-Nonself Discrimination in a Computer. In: Proceedings of IEEE Svmposiimi on Research in Secwitv and Privacy (1994)
Li, T., Liu, X., Li, H.: A New Model for Dynamic Intrusion Detection. In: Desmedt, Y.G., Wang, H., Mu, Y., Li, Y. (eds.) CANS 2005. LNCS, vol. 3810, pp. 72–84. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zeng, T. et al. (2006). Mining h-Dimensional Enhanced Semantic Association Rule Based on Immune-Based Gene Expression Programming. In: Feng, L., Wang, G., Zeng, C., Huang, R. (eds) Web Information Systems – WISE 2006 Workshops. WISE 2006. Lecture Notes in Computer Science, vol 4256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11906070_5
Download citation
DOI: https://doi.org/10.1007/11906070_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47663-4
Online ISBN: 978-3-540-47664-1
eBook Packages: Computer ScienceComputer Science (R0)