Failures with different root causes can greatly disrupt multi-fault localization, therefore, categorizing failures into distinct groups according to the culprit fault is highly important. In such a \textbf{failure indexing} task, the crux lies in the failure proximity, which comprises two points, i.e., how to effectively represent failures (e.g., extract the signature of failures) and how to properly measure the distance between those proxies for failures. Existing research has proposed a variety of failure proximities. The majority of them extract signatures of failures from execution coverage or suspiciousness ranking lists, and accordingly employ the Euclid or the Kendall tau distances, etc. However, such strategies may not properly reflect the essential characteristics of failures, thus resulting in unsatisfactory effectiveness. In this paper, we propose a new failure proximity, namely, program variable-based failure proximity, and further present a novel failure indexing approach, ReClues. Specifically, ReClues utilizes the run-time values of program variables to represent failures, and designs a set of rules to measure the similarity between them. Experimental results demonstrate the competitiveness of ReClues: it can achieve 44.12% and 27.59% improvements in faults number estimation, as well as 47.56% and 26.27% improvements in clustering effectiveness, compared with the state-of-the-art technique in this field, in simulated and real-world environments, respectively.