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Evolutionary Intelligence
Erschienen in: Evolutionary Intelligence 4/2021

21.05.2020 | Research Paper

Genetic algorithm-based fuzzy clustering applied to multivariate time series

verfasst von: Karine do Prado Ribeiro, Cristiano Hora Fontes, Gabriel Jesus Alves de Melo

Erschienen in: Evolutionary Intelligence | Ausgabe 4/2021

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Abstract

Despite the fact that the fuzzy clustering of time series based on genetic algorithm (GA) is mostly used in applications involving univariate time series, this paper presents an approach based on GA and Fuzzy C-Means (FCM) for clustering multivariate time series. Each chromosome is an individual or solution which encodes the clusters' centroids (patterns) and a bi-criterion constrained clustering is proposed to maximize both the similarity of objects in the same cluster (based on the SPCA metric) and the distance between the centers of the clusters. The proposed method is applied in two case studies involving a real industrial case which comprises pattern recognition for detecting operation failures in a gas turbine and a well-known benchmark industrial system (Tennessee Eastman process) used to evaluate techniques for detecting and diagnosing failures. The proposed approach was able to obtain better classification results compared to FCM based on classical optimization methods.
Vorheriger Artikel Enhancing Cartesian genetic programming through preferential selection of larger solutions
Nächster Artikel Self-adaptive learning for hybrid genetic algorithms

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Metadaten
Titel
Genetic algorithm-based fuzzy clustering applied to multivariate time series
verfasst von
Karine do Prado Ribeiro
Cristiano Hora Fontes
Gabriel Jesus Alves de Melo
Publikationsdatum
21.05.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Evolutionary Intelligence / Ausgabe 4/2021
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-020-00422-8

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