In this paper, a new approach is proposed for extracting localized spatio-temporal patterns from Foursquare, which is a location-based social networking system (SNS). Previously, we have proposed a method estimating the probabilistic distribution of users in Foursquare by a diffusion-type formula and have extracted various spatio-temporal patterns from the distribution by principal component analysis. However, as the distribution was the average over all the users, only the “global” patterns were extracted. So, we can not extract localized patterns showing the detailed behaviors of limited users in local areas. In this paper, a new method is proposed in order to extract the localized patterns by clustering users. First, the distance among users is measured by the Hellinger distance among the distributions of each user. Next, Ward's method (which is a widely used method in hierarchical cluster analysis) is applied to the users with their distance. Finally, the spatio-temporal patterns are extracted from the distributions for each cluster of users. The results on the real Foursquare dataset show that the proposed method can extract various and interesting localized patterns from each cluster of users.