This work is developed over the question ”How to automatically create a good clustering on spatial dataset with high different local densities?” opened by previus work of Berzi [5]. To answer the main question, this work describe a approach of recursive clustering process based on a technique of finding ”vague-solution”, where the output is an hierarchical clustering of initial dataset. In particularly the the approach is developed and tested on DBSCAN algorithm [11] with large dataset gathered by Google Place in Metropolitan Area of Milan. The core solutions developed in this algorithm are condensed in the capacity of generation a Hierarchical Clustering with a recursive select the best solutions in according tothe our goals, previously dfined by some sets of rules. The algorithm described here, and developed in my Master Thesis, rosolve two problem: - When we use an algorithm of clustering that can create a set of differents clustering, but equally valid and don’t know exctly what we must have as good solution, we areled to ask ourselves: ”What are the better?” This obviously depends by our goals, sonow the question is: ”What are our goals?”; - The second problem is condensed in the sentence ”Not all clusters can be found in one-shot clustering process, more often we must reapply the process to some part of datataset”, so there we have a second question that this paper answering: ”How to create clustering of data with ad-hoc processing for each different part of input dataset?”; These questions are resolved by the approaches named in this work as: Space of Solutions, Vague-Solution, Vague-Solution finding Method and finaly Recursive Clustering. All of these approaches was drafted and tested in mine Master Thesis titled ”Geospatial data analysis for Urban informatics applications: the case of the Google Place of the City of Milan” [25].