A conceptual version of the K-means algorithm
Pattern Recognition Letters
Structuralization of universes
Fuzzy Sets and Systems
Mathematical algorithms for the supervised classification based on fuzzy partial precedence
Mathematical and Computer Modelling: An International Journal
Conceptual k-means algorithm based on complex features
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
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In this paper, a new conceptual algorithm for the conceptual analysis of mixed incomplete data sets is introduced. This is a logical combinatorial pattern recognition (LCPR) based tool for the conceptual structuralization of spaces. Starting from the limitations of the elaborated conceptual algorithms, our laboratories are working in the application of the methods, the techniques, and in general, the philosophy of the logical combinatorial pattern recognition with the task to improve those limitations. An extension of Michalski's concept of l-complex for any similarity measure, a generalization operator for symbolic variables, and an extension of Michalski's refunion operator are introduced. Finally, the performance of the RGC algorithm is analyzed. A comparison with several known conceptual algorithms is presented.