Abstract
The family orientation process in Cuban Schools for children with Affective – Behavioral Maladies (SABM) involves clustering and classification of mixed type data with non-symmetric similarity functions. To improve this process, this paper includes some novel characteristics in clustering and prototype selection. The proposed approach uses a hierarchical clustering based on compact sets, making it suitable for dealing with non-symmetric similarity functions, as well as with mixed and incomplete data. The proposal obtains very good results on the SABM data, and over repository databases. In addition, the proposed clustering method is able to detect the true partitions of data and it was significantly better with respect to others according to external validity indexes. In prototype selection, the proposal obtains a highly reduced prototype set, while maintains the original classifier accuracy.
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.
References
Ruiz-Shulcloper, J., Abidi, M.A.: Logical Combinatorial Pattern Recognition – A Review. In: Pandalai, S.G. (ed.) Recent Research Developments in Pattern Recognition. Transword Research Networks, pp. 133–176 (2002)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Upper Saddle River (1988)
Triguero, I., Derrac, J., García, S., Herrera, F.: A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(1), 86–100 (2012)
Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Databases. Department of Information and Computer Science. University of California at Irvine, Irvine (1998)
Roy, D.K., Sharma, L.K.: Genetic K-Means Clustering Algorithm for Mixed Numeric and Categorical Datasets. International Journal of Artificial Intelligence & Applications 1(2), 23–28 (2010)
Ahmad, A., Dey, L.: A K-Means Type Clustering Algorithm for Subspace Clustering of Mixed Numeric and Categorical Data. Pattern Recognition Letters 32(7), 1062–1069 (2011)
Hattori, K., Takanashi, M.: A New Edited K-Nearest Neighbor Rule in the Pattern Classification Problem. Pattern Recognition 33(3), 521–528 (2000)
Chou, C.H., Kuo, B.A., Cheng, F.: The Generalized Condensed Nearest Neighbor Rule as a Data Reduction Technique. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 2, pp. 556–559. IEEE (2006)
Olvera-López, J.A., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: Prototype Selection Via Prototype Relevance. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 153–160. Springer, Heidelberg (2008)
García-Borroto, M., Villuendas-Rey, Y., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: Finding Small Consistent Subset for the Nearest Neighbor Classifier Based on Support Graphs. In: Bayro-Corrochano, E., Eklundh, J.-O. (eds.) CIARP 2009. LNCS, vol. 5856, pp. 465–472. Springer, Heidelberg (2009)
Wilson, R.D., Martinez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Villuendas-Rey, Y., Rey-Benguría, C., Caballero-Mota, Y., García-Lorenzo, M.M. (2012). Nearest Prototype Classification of Special School Families Based on Hierarchical Compact Sets Clustering. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_67
Download citation
DOI: https://doi.org/10.1007/978-3-642-34654-5_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34653-8
Online ISBN: 978-3-642-34654-5
eBook Packages: Computer ScienceComputer Science (R0)