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Mobile location data is an important source for understanding user profiles, helping providers deliver better services. With this kind of data, it is possible to identify the relevant points of a user, and even classify these points as places of home or work. With this knowledge, mobile service providers can increase customer engagement and retention. However, identifying and classifying points of interest (PoI) is not a trivial task, and most existing work assumes that data should be collected at a high frequency, making the process difficult and expensive. In this paper, we propose approaches to identify and classify PoIs based on sparse data, that is, they were collected at long time intervals. The results, when compared with literature solutions, show improvements of at least 13% in the accuracy for the identification of PoIs, and 10% and 4% in the classification of home and work points, respectively.

Identificação e Classificação de Pontos de Interesse Individuais com Base em Dados Esparsos