Kernelized Fuzzy c-Means for Data with Tolerance based on Opposite Criterions

Abstract

In this paper, two new clustering algorithms are proposed for the data with some errors. In any of these algorithms, the error is interpreted as one of decision variables - called "tolerance" - of a certain optimization problem like the previously proposed algotithm, but the tolerance is determined based on the opposite criterion to its corresponding previously proposed one.