The primary starting point of rough k-means(RKM) and its derivatives is how to measure and process the data objects in the boundary regions. The traditional RKM algorithm is more sensitive to the choice of the weight coefficients of the upper and lower approximations, and the partitioning results are easy affected by the non-competitive objects in boundary region. By introducing the Laplace's principle of indifference for measuring the objects in boundary regions, the aforementioned problems of the traditional RKM algorithm are sovled well by using the uppiRKM algorithm. However, the overlapping degree in boundary regions and spatial distributions of different boundary objects are not considered. In order to better describe data objects in boundary regions, the local fuzzy measurement is introduced, and an improved uppiRKM clustering algorithm based on local fuzzy enhancement of boundary region is developed. The effectiveness of the proposed algorithm is demonstrated by experimental comparison and analysis.