Reference counts are widely employed in large-scale low-level systems including Linux kernel to manage shared resources, and should be consistent with the number of objects referring to resources. Otherwise, bugs of improper update of reference counts may be caused, and resources can never be released or will be released earlier. To detect improper updates of reference counts, available static detection methods have to know the functions which increase reference counts or decrease the counts. However, manually collecting prior knowledge of reference counts is too time-consuming and may be incomplete. Though mining-based methods can reduce the dependency on prior knowledge, it is difficult to effectively detect path-sensitive bugs containing improper updates of reference counts. To this end, this study proposes a method RTDMiner that deeply integrates data mining into static analysis to detect improper updates of reference counts. First, according to the general principles of reference counts, the data mining technique is leveraged to identify functions that raise or reduce reference counts. Then, a path-sensitive static analysis method is employed to detect defective paths that increase reference counts instead of decreasing the counts. To reduce false positives, the study adopts the data mining technique to identify exceptional patterns during detection. The experiment results on the Linux kernel demonstrate that the proposed method can automatically identify functions increasing or decreasing reference counts with the precision of nearly 90%. Moreover, 24 out of the top 50 suspicious bugs detected by RTDMiner have been confirmed to be real bugs by kernel maintainers.