The watershed rehabilitation success rate have not been up, is the result of policies in watershed rehabilitation strategies that are less precise. From the above problems, we need a study that can provide a reference or any other alternative in determining priority watersheds to be rehabilitated, one through data mining. This paper uses a case study of Watershed data which are grouped using K-modes clustering algorithm based on its characteristics parameters. Watershed groupped using K-modes clustering then optimized using Davies-Bouildin Index (DBI) to get the number of clusters with the optimal level of similarity and visualized using GIS to obtain distribution maps. From trial on the Watershed of Tondano It was known that the cluster number four (4) is the optimal cluster number with an average DBI value of 0.672778, or 19.93%. The clustering results show that the wateshed in cluster 3 with 332 watershed which mostly scattered in the South Minahasa (24.7%) is a critical watershed compared to other clusters. the result of the clustering process is not much different or 90.64% similar when compared to the calculation of the watershed manually, that can be used as alternative to other reference in planning the rehabilitation of the watershed.