The key to improve the quality of incremental clustering is how to assign the new data to different clusters on the basis of original data clustering results. The existing incremental clustering algorithms mostly consider the location distribution of the newly added data point, and ignore the belonging information of the neighbor points around the new data point. To deal with the uncertain information of new data points that fall into boundary regions of original clusters, based on the rough K-means clustering, a rough K-means incremental clustering algorithm is developed. In this algorithm, focusing on the assignment of the newly added data in the boundary region, the neighborhood belonging information of the new data is taken into consideration, so that the hybrid measure of the new data point belonging to different clusters is more reasonable. Furthermore, the clusters will be merged or split to make the new divided clusters becoming more reasonable according to the cluster structure changes caused by the new data. The validity of the proposed algorithm is demonstrated by the experimental results on the artificial data sets and UCI standard data sets.