The distance-based clustering is one of the common methods to realize the anomaly detection of telemetry parameters in complex systems, such as spacecraft. However, when it is applied to high-dimensional remote measurement data, it often exposes serious problems, such as low efficiency and degraded accuracy. To overcome the difficulty in anomaly detection on the high-dimensional telemetry data, this article proposes an improved distance definition based on coupling adaptation. The inductive monitoring system (IMS) algorithm which is a classical distance clustering algorithm is improved. Based on the intrinsic distribution characteristics of historical telemetry data, this method mines dynamically the couplings among telemetry parameters while clustering. Then, it takes efficiently advantage of this mined knowledge of telemetry parameters’ couplings into the following task of anomaly detection. Finally, this article evaluates the application of the proposed method on a high-dimensional telemetry data of a real rocket power supply system. Compared with a variety of classic high-dimensional anomaly detection methods based on IMS algorithms, this article demonstrates its advantages for high-dimensional anomaly detection as well, which is 69. 03% and 41. 83% better than the best method in other two categories of IMS algorithms respectively on efficacy and accuracy of anomaly detection. It shows the superiority of the detection methods using the proposed distance definition in efficiency and accuracy.