We propose a method for discovery of composite events in videos. The algorithm processes a set of primitive events such as simple spatial relations between objects obtained from a tracking system and outputs frequent event patterns which can be interpreted as frequent composite events. We use the APRIORI algorithm from the field of data mining for efficient detection of frequent patterns. We adapt this algorithm to handle temporal uncertainty in the data without losing its computational effectiveness. It is formulated as a generic framework in which the context knowledge is clearly separated from the method in form of a similarity measure for comparison between two video activities and a library of primitive events serving as a basis for the composite events.