A stable and unsupervised Fuzzy C-Means for data classification
Akar Hawree Taher, Kacem Chehdi, Claude Cariou · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
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.