Mining Partially-Ordered Sequential Rules Common to Multiple Sequences

Mining Partially-Ordered Sequential Rules Common to Multiple Sequences

Abstract— Mining Partially-Ordered Sequential Rules Common to Multiple Sequences. Sequential rule mining is an important data mining problem with multiple applications. An important limitation of algorithms for mining sequential rules common to multiple sequences is that rules are very specific and therefore many similar rules may represent the same situation. This can cause three major problems: (1) similar rules can be rated quite differently, (2) rules may not be found because they are individually considered uninteresting, and (3) rules that are too specific are less likely to be used for making predictions. To address these issues, we explore the idea of mining “partially-ordered sequential rules” < Final Year Projects 2016 > POSR, a more general form of sequential rules such that items in the antecedent and the consequent of each rule are unordered.