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Cluster Computing

Erschienen in:

11.11.2017

Fuzzy c-means clustering algorithm for performance improvement of ENN

verfasst von: Yu Zhou, Qinchai Ren

Erschienen in: Cluster Computing | Sonderheft 5/2019

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Abstract

In this work, with the purpose of improving the performance of extension neural network (ENN), we use Fuzzy c-means (FCM) clustering algorithm to locate the initial centers of every class before the training. In traditional ENN, the initial centers are defined simply by the average values of the minimum and maximum of every characteristic. Our proposed FENN (FCM–ENN) in this paper is different from tradition ENN, and the initial centers of every class are determined by the cluster centers of FCM clustering algorism. Our proposed strategy can reflect the actual training data distribution information, thereby the performance of ENN by using this strategy is more approach to practical situation. Compared with traditional ENN, the proposed FENN has a better performance. Experimental results from three different examples, including an artificial data set, a benchmark data set and a practical application, verify the effectiveness and applicability of our proposed work.

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Titel: Fuzzy c-means clustering algorithm for performance improvement of ENN
verfasst von: Yu Zhou
Qinchai Ren
Publikationsdatum: 11.11.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 5/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1346-x

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