Modeling Relational Data as Graphs for Mining

摘要: The focus of this paper is to develop algorithms and a framework for modeling transactional data stored in relational database into graphs mining. Most the real-world transactions (e.g., money withdrawal, travel, phone calls) are recorded as individual which needs be transformed graph based on structural relationships embedded them. We present representation that not only preserves all information database, but also removes ambiguity redundancy. suite space- time-efficient from data. Extensive experimental analysis shows scalability our approaches. From pragmatic viewpoint, separates database-specific aspects make it applicable systems. Real-world has been used generating mining them various patterns.