Significant research work has been conducted in the area of process mining leading to mature solutions for discovering knowledge from structured process event logs analysis. Recently, there have been several initiatives to extend the scope of these analysis to consider heterogeneous and unstructured data sources. More precisely, email analysis has attracted much attention as emailing system is considered as one of the principal channel to support the execution of business processes (BP). However, given the unstructured nature of email logs data, process mining techniques could not be applied directly; thus it is necessary to generate structured event logs. Activities form a cornerstone component of BP that must be identified to obtain such structured logs. In this paper we propose to discover frequent activities from email logs. Existing approaches are usually supervised or require human intervention. In addition, they do not take into consideration activity business data (BD) perspective. In this paper, we introduce a pattern discovery based approach to tackle these limitations; we suggest mainly to discover frequent activities and their BD with unsupervised way. Additionally, our approach allows the detection of multiple activities per email and the automatic generation of their names while reducing human intervention. We validate our work using a public email dataset. We publicly provide our results to be a first step towards ensuring reproducibility in the studied area, which allows more practical analysis for further research.