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Proceedings Paper

A stable and unsupervised Fuzzy C-Means for data classification
Author(s): Akar Taher; Kacem Chehdi; Claude Cariou
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Paper Abstract

In this paper a stable and unsupervised version of FCM algorithm named FCMO is presented. The originality of the proposed FCMO algorithm relies: i) on the usage of an adaptive incremental technique to initialize the class centres that calls into question the intermediate initializations; this technique renders the algorithm stable and deterministic, and the classification results do not vary from a run to another, and ii) on the unsupervised evaluation criteria of the intermediate classification result to estimate the optimal number of classes; this makes the algorithm unsupervised. The efficiency of this optimized version of FCM is shown through some experimental results for its stability and its correct class number estimation.

Paper Details

Date Published: 30 April 2015
PDF: 7 pages
Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 953414 (30 April 2015); doi: 10.1117/12.2182595
Show Author Affiliations
Akar Taher, IETR, CNRS, ENSSAT, Univ de Rennes I (France)
Kacem Chehdi, IETR, CNRS, ENSSAT, Univ de Rennes I (France)
Claude Cariou, IETR, CNRS, ENSSAT, Univ de Rennes I (France)


Published in SPIE Proceedings Vol. 9534:
Twelfth International Conference on Quality Control by Artificial Vision 2015
Fabrice Meriaudeau; Olivier Aubreton, Editor(s)

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