Relational rule learning in decoupled heterogeneous subspaces

Service business now plays increasingly important role in real-world economy. This has stimulated the analytic requirement for generating insight from the structural and inter-related service data, so as to improve service operation and management excellence. In this paper, we propose a novel multi-relational classification algorithm, namely RSCC (Relational Subspace Collaborative Classification). RSCC restructures the relational dataset into a set of decoupled semantic-level subspaces while keeps the heterogeneity of relational data. It employs a heuristic rule learning strategy that globally searches for the best predicates effectively. Our experiments on multiple benchmark datasets demonstrate its performance and efficiency. © 2012 IEEE.