Multi-relational concept discovery anus to find the relational rules that best describe the target concept. An important challenge that relational knowledge discovery systems face is intractably large search space and there is a trade-off between printing the search space for fast discovery and generating high quality rules. Combining ILP approach with conventional association rule mining techniques provides effective printing mechanisms. Due to the nature of Apriori algorithm, the facts that do not have common attributes with the target concept are discarded. This leads to efficient pruning of search space. However, tinder certain conditions, it, fails to generate transitive rules, which is an important, drawback when transitive rules are the only way to describe the target concept. In this work, we analyze the effect of incorporating unrelated facts for generating transitive rules in an hybrid relational concept discovery system, namely C(2)D, which combines ILP and Apriori.