Using Answer Set Programming for pattern mining
  Serial pattern mining consists in extracting the frequent sequential patterns from a unique sequence of itemsets. This paper explores the ability of a declarative language, such as Answer Set Programming (ASP), to solve this issue efficiently. We propose several ASP implementations of the frequent sequential pattern mining task: a non-incremental and an incremental resolution. The results show that the incremental resolution is more efficient than the non-incremental one, but both ASP programs are less efficient than dedicated algorithms. Nonetheless, this approach can be seen as a first step toward a generic framework for sequential pattern mining with constraints. 
Thomas Guyet (INRIA - IRISA), Yves Moinard (INRIA - IRISA), Ren\'e   Quiniou (INRIA - IRISA)
cs.AI cs.DB cs.LO