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While much work has been done in finding linear correlation among subsets of features in high-dimensional data, work on detecting nonlinear correlation has been left largely untouched. In this paper, we present an algorithm for finding and visualizing nonlinear correlation clusters in the subspace of high-dimensional databases.Unlike the detection of linear correlation in which clusters are of unique orientations, finding nonlinear correlation clusters of varying orientations requires merging clusters of possibly very different orientations. Combined with the fact that spatial proximity must be judged based on a subset of features that are not originally known, deciding which clusters to be merged during the clustering process becomes a challenge. To avoid this problem, we propose a novel concept called co-sharing level which captures both spatial proximity and cluster orientation when judging similarity between clusters. Based on this concept, we develop an algorithm which not only detects nonlinear correlation clusters but also provides a way to visualize them. Experiments on both synthetic and real-life datasets are done to show the effectiveness of our method.