Abstract
This paper introduces a new method of fuzzy semisupervised hierarchical clustering using fuzzy instance level constraints. It introduces the concepts of fuzzy must-link and fuzzy cannot-link constraints and use them to find the optimum α-cut of a dendrogram. This method is used to approach the problem of classifying scientific publications in web digital libraries. It is tested on real data from that problem against classical methods and crisp semisupervised hierarchical clustering.
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Diaz-Valenzuela, I., Martin-Bautista, M.J., Vila, MA. (2014). A Fuzzy Semisupervised Clustering Method: Application to the Classification of Scientific Publications. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 442. Springer, Cham. https://doi.org/10.1007/978-3-319-08795-5_19
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DOI: https://doi.org/10.1007/978-3-319-08795-5_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08794-8
Online ISBN: 978-3-319-08795-5
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