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Granular Computing
Erschienen in: Granular Computing 4/2021

25.07.2020 | Original Paper

Noise-resistant fuzzy clustering algorithm

verfasst von: S. Askari

Erschienen in: Granular Computing | Ausgabe 4/2021

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Abstract

The main objective of Fuzzy C-means (FCM) algorithm is to group data into some clusters based on their similarities and dissimilarities. However, noise and outliers affect the performance of the algorithm that results in misplaced cluster centers. Although several corrections are made in the algorithm to tackle this problem but the algorithm is not improved effectively and still suffers from the same problem. Noise-resistant FCM (nrFCM) algorithm is proposed in this work to improve the performance of the FCM algorithm when dealing with noise and outliers. The nrFCM algorithm eliminates the effects of noise and outliers on the cluster centers by introducing a function of distance instead of the distance itself into the objective function of the FCM algorithm. It is shown that the nrFCM algorithm is significantly more accurate than the FCM algorithm and noise and outliers cannot impair its accuracy. However, its runtime is higher than that of the FCM algorithm because of nonlinear update equation for cluster centers.
Vorheriger Artikel Spatial and temporal reasoning with granular computing and three way formal concept analysis
Nächster Artikel Numerical solution of two-dimensional nonlinear fuzzy delay integral equations via iterative method and trapezoidal quadrature rule

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Metadaten
Titel
Noise-resistant fuzzy clustering algorithm
verfasst von
S. Askari
Publikationsdatum
25.07.2020
Verlag
Springer International Publishing
Erschienen in
Granular Computing / Ausgabe 4/2021
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-020-00230-6

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