Structure Discovery in Sequentially-Connected Data Streams
Much of current data mining research is focused on discovering sets of attributes that discriminate data entities into classes, such as shopping trends for a particular demographic group. In contrast, the authors are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Their research is particularly applicable to domains in which the data is event driven, such as counter-terrorism intelligence analysis. In this paper they describe an algorithm designed to operate over relational data received from a continuous stream.