As an effective tool for data mining, formal concept analysis can yield interpretable decision rules using attribute reduction. Currently, existing reduction methods within the framework of formal concept analysis result in all samples sharing the same conditional attributes, thereby overlooking the distinctions between rules extracted from different samples. To address this problem, we present a novel attribute reduction method for single samples. For this purpose, a single sample-oriented reduction framework is established by incorporating a discernibility attribute vector and an algorithm for calculating reducts based on the matrix representation of the discernibility attribute vector. Furthermore, the process of obtaining a new reduct from the original reduct is discussed by considering incremental learning. Given that attributes included in reducts are crucial conditions for decision-making, intent-minimal granular rules are generated through attribute reduction, and a reduction-based method for measuring the completeness of the rule set is discussed. Finally, to accomplish the classification task, a minimal rule-based classification model (MRCM) is proposed. The experimental results show that the MRCM has the same classification ability as the unreduced rules, and the rules with higher completeness tend to exhibit better classification performance.