Finding Cyclic Patterns on Sequential Data

The need for the study of dynamic and evolutionary settings made time a major dimension when it comes to data analytics. From business to health applications, being able to understand temporal patterns of customers or patients can determine the ability to adapt to future changes, optimizing processes and support other decisions. In this context, different approaches to Temporal Pattern Mining have been proposed in order to capture different types of patterns able to represent evolutionary behaviors, such as regular or emerging patterns. However, these solutions still lack on quality patterns with relevant information and on efficient mining methods. In this paper we propose a new efficient sequential mining algorithm, named PrefixSpan4Cycles, for mining cyclic sequential patterns. Our experiments show that our approach is able to efficiently mine these patterns when compared to other sequential pattern mining methods such as the GenPrefixSpan and PrefixSpan. Also for datasets with a significant number of regularities, our algorithm performs efficiently, even dealing with significant constraints regarding the nature of cyclic patterns. Keywords—temporal pattern mining; cyclic patterns; sequential