Wi-Fi fingerprint acquisition and modeling is a time-consuming work, while crowdsourcing is an effective way to solve this problem. The feasibility of unsupervised clustering was demonstrated for Place of Interest (POI) positioning, which is benefit to generate radio map by crowded source. At first, a framework of Wi-Fi fingerprint localization algorithm was given, then the k-means, affinity propagation and adaptive propagation were applied to this framework. Using BP neural network as a supervised learning reference, an evaluation was executed in a laboratory to analyze the relationship between indoor POI partition and spatial division, and the Radio Signal Strength Indications (RSSI) were collected in POI. Compared the clustering results in the POI spatial space, the recall and the precision of the three clustering algorithms were both over 90%. The experimental results show that the unsupervised clustering method is an effective solution for coarse-grained POI indoor positioning application.