Since Thoughts on Bayesian Networks I've been thinking about using Bayesian networks as a means to identify situations.
In How Bayesian Networks learn, various conditions, aspects, circumstances or situational occurrences are captured within a single observation. For example, if you were observing/recording weather conditions, each observation could be composed of co-occurring aspects, such as the current humidity reading, the sunshine level, or whether it is raining or cloudy. These co-occurring aspects might well be thought of as defining particular situations.
These co-occurring aspects that make up particular situations (combinations of co-occurring aspects) can be used to predict/infer the probability of one particular aspect occurring in the future, given that we know how often combinations of their co-occurring aspects occur at the same time. The more frequently they occur, the more probable the prediction. The process used to work this out is called marginalising over the priors, and the process requires computing a Conditional Probability Table (CPT) for the particular target aspect you are looking to predict. In doing so, this table derives general, relative probabilities for your target aspect based on the various combinations of other aspects that were also present when your target aspect was present.
We might then try to model situations as the association of captured momentary aspects (as events) and identify recurring aspects, thereby revealing/discovering situations as they occur more frequently.