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On the Application of Bio-Inspired Optimization Algorithms to Fuzzy C-Means Clustering of Time Series

Permanent link
https://hdl.handle.net/10037/8980
DOI
https://doi.org/10.5220/0005276203480353
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Date
2015
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Muhammad Fuad, Muhammad Marwan
Abstract
Fuzzy c-means clustering (FCM) is a clustering method which is based on the partial membership concept. As with the other clustering methods, FCM applies a distance to cluster the data. While the Euclidean distance is widely-used to perform the clustering task, other distances have been suggested in the literature. In this paper we study the use of a weighted combination of metrics in FCM clustering of time series where the weights in the combination are the outcome of an optimization process using differential evolution, genetic algorithms, and particle swarm optimization as optimizers. We show how the overfitting phenomenon interferes in the optimization process that the optimal results obtained during the training stage degrade during the testing stage as a result of overfitting.
Description
Published version. Source at http://doi.org/10.5220/0005276203480353.
Publisher
INSTICC
Citation
De Marsico, Maria; Figueiredo, Mario; Fred, Ana [Eds.] ICPRAM 2015 Proceedings of the International Conference on Pattern Recognition Applications and Methods Volum 1 p. 348-353, SciTePress, 2015
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