On interactive pattern mining from relational databases

In this paper we introduce a general framework for a constraint based querying system devised with the aim of supporting the intrinsically exploratory (i.e., human-guided, interactive, iterative) nature of pattern discovery. Our framework provides users with an expressive constraint based query language which allows the discovery process to be effectively driven toward potentially interesting patterns. Such constraints are also exploited to reduce the cost of pattern mining computation. According to this framework, we implemented a comprehensive mining system, accomplishing the requirement of the knowledge discovery process. The system can access real world relational databases from which extract data. After a preprocessing step, users queries are answered by an efficient pattern mining engine which entails several data and search space reduction techniques. Finally, results are presented to the user, and then stored in the database. New user-defined constraints can be easily added to the system in order to target the particular application considered.