Enhanced situation space mining for data streams

Data streams can capture the situation which an actor is experiencing. Knowledge of the present situation is highly beneficial for a wide range of applications. An algorithm called pcStream can be used to extract situations from a numerical data stream in an unsupervised manner. Although pcStream outperforms other stream clustering algorithms at this task, pcStream has two major flaws. The first is its complexity due to continuously performing principal component analysis (PCA). The second is its difficulty in detecting emerging situations whose distributions overlap in the same feature space.