Data mining plays an important role in collecting information to make businesses more competitive in present business world. It is seen that the location of any business outlet is a major factor of its success. Establishing different business enterprises include a detail study of localities, people's income status living in those areas, and many other non-spatial factors. This paper is one such idea to suggest those locations for entrepreneurs, based on which they can decide on the where they can setup their business outlet. The proposed algorithm makes use of Delaunay triangulation for capturing spatial proximity and Bezier curves are used to model obstacles. The algorithm is implemented as Web application, which accepts the name of a place and collects data, form clusters and show the feasible locations of the service specified, considering the geographic irregularities and man-made obstructions. In this algorithm, spatial and non-spatial data related to a location are collected and the spatial clustering algorithm is initiated which works based on the obtained data. Clusters are formed based on the unique characteristics of each location. The experimental results are conducted on many different locations of India and in this paper results are shown for three places namely, Mysuru, Patna and Mumbai. The results have shown expected and exciting results.