Clustering is a method to group similar events. This can be done automatically for you using several well known algorithms, which each have their own pros and cons.
You can automatically generate new clusters for an FCS file by selecting the Clustering Tool. Then choose a clustering algorithm to use, fill in your desired parameters and click "Apply".
You can combine clusters by dragging and dropping the file tree entry for one cluster onto another. This takes the events from the dragged clusters and adds them to the cluster you drop them on.
K-Means is a very basic clustering algorithm. One of its biggest benefits it that is is very fast at least compared to other clustering algorithms. It can assign clusters a little crudely at times and you will be required to input how many clusters you want it to find. If you select K-Means as your clustering algorithm, you will need to provide the following:
DBSCAN is density-based spatial clustering of applications with noise. It has some features that make it a little more convenient than K-Means. First, DBSCAN does not require you to specify the number of clusters. It will figure out how many clusters are in the data on its own. Next, DBSCAN is good at handling outliers in the data and will label data as noise if appropriate, not including them in a cluster.