With location-based social network (LBSN) flourishing, location check-in records offer us sufficient information resource to do relative mining. Among locations visited by a user, those attracting relatively more visits from that user can serve as a support for further mining and improvement for location-based services. Therefore, great significance lies in the partition for visited locations based on a user’s visiting frequency. The aim of our paper is to partition locations for individual users by utilizing classification in machine learning, categorizing the location for a user once he or she makes initial check-in there. After feature extraction for each initial check-in record, we evaluate the contribution of three feature categories. The results show the contribution of different feature categories varies in classification, where social features appear to offer the least contribution. At last, we do a final test on the whole sample, comparing the results with two baselines based on majority voting respectively. The results largely outperform the baselines in general, demonstrating the effectiveness of classification.