The recent introduction of the big data paradigm and advancements in machine learning and deep mining techniques have made proof guidance and automation in interactive theorem provers (ITPs) an important research topic. In this paper, we provide a learning approach based on sequential pattern mining (SPM) for proof guidance in the PVS proof assistant. Proofs in a PVS theory are first abstracted to a computer-processable corpus. SPM techniques are then used on the corpus to discover frequent proof steps and proof patterns, relationships of proof steps / patterns with each other, dependency of new conjectures on already proved facts and to predict the next proof step(s). Obtained results suggest that the integration of SPM in proof assistants can be used to guide the proof process and in the development of proof tactics/strategies.