We propose a novel method for rapid pattern analysis of high-dimensional numerical data, termed tunnel clustering. The main advantages of the method are its relatively low computational complexity, endogenous determination of cluster composition and number, and a high degree of interpretability of final results. We present descriptions of three different variations: one with fixed hyperparameters, an adaptive version, and a combined approach. Three fundamental properties of tunnel clustering are examined. Practical applications are demonstrated on both synthetic datasets containing 100 000 objects and on classical benchmark datasets.