Historically, data mining research has been focused on discovering sets of attributes that discriminate data entities into classes or association rules between attributes. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event driven, such as counter-terrorism intelligence analysis. In this paper we describe an algorithm designed to operate over relational data received from a continuous stream. Our approach includes a mechanism for summarizing discoveries from previous data increments so that the globally best patterns can be computed by examining only the new data increment. We then describe a method by which relational dependencies that span across temporal increment boundaries can be efficiently resolved so that additional pattern instances, which do not reside entirely in a single data increment, can be discovered. We also describe a method for change detection using a measure of central tendency designed for graph data. We contrast two formulations of the change detection process and demonstrate the ability to identify salient changes along meaningful dimensions and recognize trends in a relational data stream.
Received Revised AcceptedMuch 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, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event-driven or relationally structured. In this paper we present approaches to address two related challenges; the need to assimilate incremental data updates and the need to mine monolithic datasets. Many realistic problems are continuous in nature and therefore require a data mining approach that can evolve discovered knowledge over time. Similarly, many problems present data sets that are too large to fit into dynamic memory on conventional computer systems. We address incremental data mining by introducing a mechanism for summarizing discoveries from previous data increments so that the globally-best patterns can be computed by mining only the new data increment. To address monolithic datasets we introduce a technique by which these datasets can be partitioned and mined serially with minimal impact on the result quality. We present applications of our work in both the counter-terrorism and bioinformatics domains.
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