Abstract
DC* (Double Clustering by A*) is an algorithm for interpretable fuzzy information granulation of data. It is mainly based on two clustering steps. The first step applies the LVQ1 algorithm to find a suitable representation of data relationships. The second clustering step is based on the A* search strategy and is aimed at finding an optimal number of fuzzy granules that can be labeled with linguistic terms. As a result, DC* is able to linguistically describe hidden relationships among available data. In this paper we propose an extension of the DC* algorithm, called DC\(^{*} _{1.1}\), which improves the generalization ability of the original DC* by modifying the A* search procedure. This variation, inspired by Support Vector Machines, results empirically effective as reported in experimental results.
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References
Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Boston, MA (2003)
Castellano, G., Fanelli, A.M., Mencar, C., Plantamura, V.L.: Classifying data with interpretable fuzzy granulation. In: Proc Of SCIS & ISIS, pp. 872–876 (2006)
Castellano, G., Fanelli, A.M., Mencar, C.: DCf: A Double Clusterng framework for fuzzy information granulation. In: Proc. of IEEE GrC-2005, Beijing, China (2005)
Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines (and other kernel-based learning methods). Cambridge University Press, New York, NY, USA (2000)
Giboin, A.: ML Comprehensibility and KBS explanation: stating the problem collaboratively. In: Proc. of IJCAI 1995 Workshop on Machine Learning and Comprehensibility, pp. 1–11 (1995)
Kohonen, T.: Self Organizing Maps. Springer Verlag, Heidelberg (1995)
Kuncheva, L.I., Steimann, F.: Fuzzy Diagnosis (Editorial). Artificial Intelligence in Medicine 16(2), 121–128 (1999)
Mencar, C., Castellano, G., Fanelli, A.M.: Balancing Interpretability and Accuracy by Multi-Level Fuzzy Information Granulation. In: Proc. of WCCI 2006, Vancouver, Canada, pp. 2157–2163 (2006)
Mencar, C.: Theory of Fuzzy Information Granulation: Contributions to Interpretability Issues, Ph.D. Thesis, University of Bari, (2005), available at www.di.uniba.it/~mencar/phdthesis.html
Russel, S.J., Norvig, P.: Artificial Intelligence: a modern Approach. Prentice-Hall Inc., New Jersey (1995)
Ster, B., Dobnikar, A.: Neural Networks in medical diagnosis: comparison with other methods. In: Proc. of EANN, pp. 427–430 (1996)
Valente de Oliveira, J.: Semantic Constraints for Membership Function Optimization. in IEEE Transactions on Systems, Man and Cybernetics, part A 29(1), 128–138 (1999)
Zhou, Z.-H.: Comprehensibility of Data Mining Algorithms. In: Encyclopedia of Data Warehousing and Mining, Wang, J.(ed.), pp. 190–195 (Hershey, PA: IGI, 2005)
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Mencar, C., Consiglio, A., Castellano, G., Fanelli, A.M. (2007). Improving the Classification Ability of DC* Algorithm. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_18
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DOI: https://doi.org/10.1007/978-3-540-73400-0_18
Publisher Name: Springer, Berlin, Heidelberg
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