Higher order mining
John F. Roddick, Myra Spiliopoulou, Daniel Lister, Aaron Ceglar · ACM SIGKDD Explorations Newsletter · 2008
The value of knowledge obtainable by analysing large quantities of data is widely acknowledged. However, so-calledprimaryorraw datamay not always be available for knowledge discovery for several reasons. First, cooperating institutions that are interested in sharing knowledge may not be willing (or allowed) to disclose their primary data. Second, data in the form of streams are only temporarily available for processing. If stored at all, stream data are maintained in the form of synopses or derived, abstract representations of the original data. Finally, even for non-stream data, there are limits on the computation speed to be achieved -- such limits are set by hardware and firmware technologies. This problem can only be partially solved through parallelization and increased processing power. Ultimately, in many cases data must be summarized to be processed efficiently. In the light of these observations, we anticipate the need for defining and practising data mining without the luxury of primary data. To that end, we formally introduce the paradigm ofHigher Order Miningas a form of data mining that is applied over non-primary, derived data or patterns. Although Higher Order Mining is a new paradigm, there are already research advances on knowledge discovery methods from patterns rather than data. We discuss them and organize them under the light of the new paradigm. We show that the HOM paradigm reveals further potential for knowledge discovery, including the delivery of rules and patterns with semantics that are closer to human intuition and are thus more appropriate for human inspection.