In this paper we describe I-Subdue, an extension to the Subdue graph-based data mining system. I-Subdue operates over sequentially received relational data to incrementally discover the most representative substructures. The ability to incrementally refine discoveries from serially acquire d data is important for many applications, particularly as computer systems become more integrated into human lives as interactive assistants. This paper describes initial wo rk to overcome the challenge of locally optimal substructures overshadowing those that are globally optimal. We conclude by providing an overview of additional challenges for sequential structure discovery.
['Jeffrey Coble', 'Diane J. Cook', 'Lawrence B. Holder', 'Runu Rathi']
Structure Discovery from Sequential Data.