"Clustering" is a very broad umbrella for a set of unsupervised techniques that tries to group data items together, according to its characteristics/covariates/variables.
Let's say you have two variables $x1$ and $x2$, and 6 samples. I could easily find two clusters with techinques such as k-Means.
When it comes to text mining, these variables are frequently associated with word frequency and/or context representation. For example, a sample may be a document and $x1$ and $x2$ may be the frequency of word "a" and word "b". Also, timestamp could be $x3$.
If you want to find clusters among documents, you need first to define a variable or "feature" extraction method (such as word frequency, tf-idf, word embedding etc). You can concatenate your text features with time-related features, and apply any clustering technique to this set of features in order to cluster your documents.
@Peter suggest you to use a topic modelling technique, which is a method for reducing the feature dimensional space (2 features = 2 dimensions, 1000 features = 1000 dimensions) after applying a word frequency feature extraction. It will help you describe each of these documents according to the frequency of a certain set of important words. Roughly speaking, a topic is a set of words that appear together. So for each document, the topics will have a relevance level.
It is not strictly a "clustering" approach, but it certainly achieves document clusterization by using the most relevant topic.
If you want to have temporal dimension coupled with topic modelling, you have to study a little more, read some papers and have some practice with these methods I mentioned. It's possible to have a pipeline with topic modelling for the word frequency ($x1$ is the relevance of topic 1 for a document, $x2$ for topic 2 ...) then you attach the timestamp to the result and apply a clustering technique such as k-Means.