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2018 | OriginalPaper | Chapter

Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data

Authors : Sara Inés Rizo Rodríguez, Francisco de Assis Tenorio de Carvalho

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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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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previous chapter Gaussian Kernel-Based Fuzzy Clustering with Automatic Bandwidth Computation
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Metadata
Title
Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data
Authors
Sara Inés Rizo Rodríguez
Francisco de Assis Tenorio de Carvalho
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_68

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