A stable and unsupervised Fuzzy C-Means for data classification

Akar Taher; Kacem Chehdi; Claude Cariou

doi:10.1117/12.2182595

30 April 2015 A stable and unsupervised Fuzzy C-Means for data classification

Akar Taher, Kacem Chehdi, Claude Cariou

Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 953414 (2015) https://doi.org/10.1117/12.2182595
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France

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.

Citation Download Citation

Akar Taher, Kacem Chehdi, and Claude Cariou "A stable and unsupervised Fuzzy C-Means for data classification", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 953414 (30 April 2015); https://doi.org/10.1117/12.2182595

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