Geometrical codification for clustering mixed categorical and numerical databases-Reference-Cited by-同舟云学术

Geometrical codification for clustering mixed categorical and numerical databases

Published:2011-12-06 Issue:1 Volume:39 Page:167-185
ISSN:0925-9902
Container-title:Journal of Intelligent Information Systems
language:en
Short-container-title:J Intell Inf Syst

Author:

Barcelo-Rico Fatima,Diez Jose-Luis

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Link

http://link.springer.com/content/pdf/10.1007/s10844-011-0187-y.pdf

Reference19 articles.

1. Ahmad, A., & Dey, L. (2007). A k-mean clustering algorithm for mixed numeric and categorical data. Data & Knowledge Engineering, 63(2), 503–527.

2. Babuska, R. (1996). Fuzzy modeling and identification. PhD dissertation, Delft University of Technology, Delft, The Netherlands.

3. Barcelo-Rico, F., & Diez, J. L. (2009). Comparative study of codification techniques for clustering heart disease database. Modeling and Control in Biomedical Systems, 7(1), 64–69.

4. Bourke, P. (1993). http://local.wasp.uwa.edu.au/ . Accessed 30 July 2010.

5. Brouwer, R. K. (2007). A method for fuzzy clustering with ordinal attributes. International Journal of Intelligent Systems, 22, 590–620.

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