Abstract
This study investigates the formulation of fuzzy logic as integrated component of the proposed model in data mining in order to classify the dataset prior to the implementation of data mining tools such summarization, association rule discovery, and prediction. The novel contribution of this paper is the fuzzification of the dataset prior to pattern discovery. The model is compared to the classical clustering, regression model, and neural network using the Internet usage database available at the UCI Knowledge Discovery on Databases (KDD) archive. Our test is anchored on parameters like relevant measure, processing performance, discovered rules or patterns and practical use of the findings. The proposed model indicates adequate performance in clustering, higher clustering accuracy and efficient pattern discovery compared with the other models.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Lee, S., Lee, T.S., Kim, H.-J., Lee, Y.K.: Development of traffic accidents prediction model with intelligent system theory. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3481, pp. 880–888. Springer, Heidelberg (2005)
Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)
Kim, J.-S., Kim, M., Noh, B.-N.: A fuzzy expert system for network forensics. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3043, pp. 175–182. Springer, Heidelberg (2004)
Crestani, F., Pasi, G.: Soft Information Retrieval: Applications of Fuzzy Set Theory and Neural Networks. In: Kasabov, N., Kozma, R. (eds.) Neuro-Fuzzy Techniques for Intelligent Information Systems, pp. 287–315. Physica Verlag (Springer Verlag), Heidelberg (1999)
Hollmen, J.: Self Organizing Map (1996), available at http://www.cis.hut.fi/~jhollmen/dippa/node9.html
UCI Knowledge Discovery in databases, available at http://kdd.ics.uci.edu/
Chen, B., Haas, P., Scheuermann, P.: A new two-phase sampling based algorithm for discovering association rules. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2002)
Han, E. H., Karypis G., Kumar, V., and Mobasher, B.: Clustering in a high-dimensional space using hypergraph models (1998), Available at http://www.informatikuni-siegen.de/~galeas/papers/general/Clustering_in_a_High-Dimensional_Space_Using_Hypergraphs_Models_28Han1997b29.pdf
Agglomerative Hierarchical Clustering, Available at http://www2.cs.uregina.ca/~hamilton/courses/831/notes/clustering/clustering.htm
Brule J. F.: Fuzzy Systems. Available at http://www.austinlinks.com/Fuzzy/tutorial.html
Saint-Paul, R., Raschia, G., Mouaddib, N.: General Purpose Database Summarization. In: Proceedings of the 31st VLDB Conference, Trondleim, Norway, pp. 733–744 (2005)
Dembele, D., Kastner, P.: Fuzzy C-Means for Clustering Microarray Data. Journal of Bioinformatics 19(8), 973–980 (2003)
The Generic Fuzzy Clustering Algorithm (2005), available at http://polywww.in2p3.fr/activities/info/doc/glast/fc.htm#2
Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (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
Gerardo, B.D., Lee, J., Joo, SC. (2006). Discovering Patterns Based on Fuzzy Logic Theory. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751632_97
Download citation
DOI: https://doi.org/10.1007/11751632_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34077-5
Online ISBN: 978-3-540-34078-2
eBook Packages: Computer ScienceComputer Science (R0)