Abstract
Correspondence analysis is a very common and renowned statistical technique, with applications in data summarization, classification, regression, etc. One particular approach is that of comparing different partitions over the same set of objects. Moreover, it can be interesting to analyze correspondences at different detail levels, not only between partitions, but between classes in these partitions. In addition, the case of fuzzy partitions over data is still a researching milestone in development. In this work we propose a novel measure following a previous definition of an alternate methodology in terms of data mining tools, in order to overcome some limitations of the former one for the case of considering partial and global correspondences between fuzzy partitions.
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Acknowledgements
This work has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (FEDER) under projects TIN2015-64776-C3-1-R and TIN2014-58227-P, and by the Energy IN TIME project funded from the European Union in the Seventh Framework Programme under grant agreement No. 608981.
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Molina, C., Ruiz, M.D., Sánchez, D., Serrano, J.M. (2017). Ad Hoc Metric for Correspondence Analysis Between Fuzzy Partitions. In: Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds) Scalable Uncertainty Management. SUM 2017. Lecture Notes in Computer Science(), vol 10564. Springer, Cham. https://doi.org/10.1007/978-3-319-67582-4_31
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