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Clustering Mixed Incomplete Data

Clustering Mixed Incomplete Data

Jose Ruiz-Shulcloper, Guillermo Sanchez-Diaz, Mongi A. Abidi
Copyright: © 2002 |Pages: 18
ISBN13: 9781930708266|ISBN10: 1930708262|EISBN13: 9781591400172
DOI: 10.4018/978-1-930708-26-6.ch006
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MLA

Ruiz-Shulcloper, Jose, et al. "Clustering Mixed Incomplete Data." Heuristic and Optimization for Knowledge Discovery, edited by Hussein A. Abbass, et al., IGI Global, 2002, pp. 89-106. https://doi.org/10.4018/978-1-930708-26-6.ch006

APA

Ruiz-Shulcloper, J., Sanchez-Diaz, G., & Abidi, M. A. (2002). Clustering Mixed Incomplete Data. In H. Abbass, C. Newton, & R. Sarker (Eds.), Heuristic and Optimization for Knowledge Discovery (pp. 89-106). IGI Global. https://doi.org/10.4018/978-1-930708-26-6.ch006

Chicago

Ruiz-Shulcloper, Jose, Guillermo Sanchez-Diaz, and Mongi A. Abidi. "Clustering Mixed Incomplete Data." In Heuristic and Optimization for Knowledge Discovery, edited by Hussein A. Abbass, Charles S. Newton, and Ruhul Sarker, 89-106. Hershey, PA: IGI Global, 2002. https://doi.org/10.4018/978-1-930708-26-6.ch006

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Abstract

In this chapter, we expose the possibilities of the Logical Combinatorial Pattern Recognition (LCPR) tools for Clustering Large and Very Large Mixed Incomplete Data (MID) Sets. We start from the real existence of a number of complex structures of large or very large data sets. Our research is directed towards the application of methods, techniques and in general, the philosophy of the LCPR to the solution of supervised and unsupervised classification problems. In this chapter, we introduce the GLC and DGLC clustering algorithms and the GLC+ clustering method in order to process large and very large mixed incomplete data sets.

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