UMLI: An Unsupervised Mobile Locations Extraction Approach with Incomplete Data

Location extraction in an indoor environment is a great challenge, and yet, it is of great interest to retrieve locations information without manually labeling them. Indoor location information, e.g. which room a user is located, is precious for applications such as location based services, mobility prediction, personal health care, network resource allocation, etc. Since the GPS signal is missing, another form of identification for each location is needed. WiFi is a potential candidate due to its easy availability. However, it is very noisy and missing excessively due to the limited range of access points. We propose a two-layer clustering method that is able to $i$) classify the rooms in an unsupervised manner; ii) handle missing data effectively. Experiment results using the real traces show UMLI can achieves an identification rate of 99.84%.