Fuzzy clustering using linguistic-valued exponent
This work addresses clustering problems in data analysis, but it appears incremental as it builds on existing FCM innovations with a specific theoretical modification.
The paper tackles the problem of improving the Fuzzy C-Means (FCM) clustering algorithm by proposing a new method that uses hedge algebra theory to model the exponent parameter, resulting in experimental validation of its practical clustering capabilities.
The purpose of this paper is to study the algorithm FCM and some of its famous innovations, analyse and discover the method of applying hedge algebra theory that uses algebra to represent linguistic-valued variables, to FCM. Then, this paper will propose a new FCM-based algorithm which uses hedge algebra to model FCM's exponent parameter. Finally, the design, analysis and implementation of the new algorithm as well some experimental results will be presented to prove our algorithm's capacity of solving clustering problems in practice.