Improving the time efficiency of ILP-based multi-relational concept discovery with dynamic programming approach

Abstract

Large amount of relational data is stored in databases. Therefore, working directly on the data stored in database is an important feature for multi-relational concept discovery systems. In addition to concept rule quality, time efficiency is an important performance dimension for concept discovery since dealing with large amount of data is a must. In this work, we present a dynamic programming based approach for improving the time efficiency on an ILP-based concept discovery system, namely CRIS (Concept Rule Induction System), which combines ILP and Apriori and directly works on databases. © 2011 Springer Science+Business Media B.V.