The extended variable precision rough sets model (VPRS l,u ) is a development of the rough set theory methodology first introduced in the seminal work by Zdzislaw Pawlak. As a technique for data analysis it is based on a set theoretical approach for the possible classification of groups of objects (condition classes) in a decision table to categorical decision attribute values (decision classes). Two measures are considered in this paper which utilize the l and u values necessary within VPRS l,u , the degree of dependency (l, u)-DoD of a decision class and the quality of classification (l, u)-QoC of objects in the model. This article introduces the notion of the (l, u)-graph, which elucidates the effect of the choice of the l and u values on the associated levels of (l, u)-DoD and (l, u)-QoC. A number of descriptive measures including specific lines are defined that utilize the information contained in the (l, u)-graphs. These measures and lines are used to intelligently identify and select subsets of condition attributes described (l, u)-reducts and a choice of the l and u values, based on retaining the underlying (l, u)-DoD or (l, u)-QoC within the associated (l, u)-graph.