Abstract
Fuzzy c-Lines (FCL) is a technique for partitioning a data set into several linear clusters whose prototypes are lines. This paper considers the linear fuzzy clustering of data with uncertainties based on intervals. The clustering criterion is defined by considering two different metrics, Minimum Distance and Maximum Distance, and the optimal prototypes are estimated by using a linear search algorithm. Numerical example shows that the result of the proposed method provides a tool for interpretation of local features of the data with uncertainties.
