Abstract
In this work we propose a clustering methodology model named as Mixture of Fuzzy Models (MFMs). We adopt two assumptions: the data points are generated by a membership function and the sum of the memberships to all of the clusters must be greater or equal than zero. The objective is to obtain a set of membership functions which represent the data. It is formulated as a multiobjective optimization problem with two objectives: to maximize the sum of memberships within each cluster and to maximize the differences of memberships between clusters.
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Pagola, M., Barrenechea, E., Jurío, A., Paternain, D., Bustince, H. (2014). Clustering Based on a Mixture of Fuzzy Models Approach. 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 443. Springer, Cham. https://doi.org/10.1007/978-3-319-08855-6_48
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DOI: https://doi.org/10.1007/978-3-319-08855-6_48
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08854-9
Online ISBN: 978-3-319-08855-6
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