Abstract
Symbolic Data Analysis provides suitable new types of variable that can take into account the variability present in the observed measurements. This paper proposes a partitioning fuzzy clustering algorithm for interval-valued data based on suitable adaptive Euclidean distance and entropy regularization. The proposed method optimizes an objective function by alternating three steps aiming to compute the fuzzy cluster representatives, the fuzzy partition, as well as relevance weights for the interval-valued variables. Experiments on synthetic and real datasets corroborate the usefulness of the proposed algorithm.
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Acknowledgment
The authors would like to thank CNPq and FACEPE (Brazilian agencies) for their financial support and the anonymous referees for their helpful suggestions.
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Rodríguez, S.I.R., de Carvalho, F.d.A.T. (2018). Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_68
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