How to find patterns in a series of timestamps I have a series of timestamps that represent the time a user clicked a certain button.

My goal is to detect the automated clicks, so I need to find recurring patterns in the data that may point to an automated script. The majority of the data is by regular users. I don't need to detect them online, I just need to detect them in historical data. I also have user_id to make a distinction between users.

Is there a default approach to detecting recurring patterns in a largely unstructured dataset?

Most things I've found are about anomaly detection on a certain value paired with a timestamp but my problem is not really anomaly-detection as I need to find patterns amongst unpatterned data
The question can be super difficult if you only have the number of clicks per time-stamp. The reason is that you might find many different recurrent patterns!
For instance certain time periods in year, month or day (according to the functionality of website) may attract a certain temporal pattern of clicks.
But if you have more relevant features the story is different.

After all I will point out some directions and hope it helps:

Autocorrelation may initially give you a good insight about what is happening there.

Then go for Time-Warping methods to define similarity function and seek it along time-series. 

Embedding in high-dimensional spaces disclose the temporal recurrent pattern of a time-series. It's mostly from physics literature and is called State-Space Recunstruction. This method transfers your time-series into a m-dimensional space using this and there you can see the time-series as a dynamic system which may help in detecting recurrent dynamics.