We propose a novel approach for the dimension reduction of high dimensional data to make the data available for conventional statistical evaluations. Our method is based on nonparametric multiple Gaussian clustering, in which we assume that in each cluster block, the instances follow an independent and identically (i.i.d.) univariate Gaussian distribution. We show theoretically that our model can fit multivariate Gaussian distributions with exchangeable features. We further show how the clusters derived with this specific model can be used to effectively reduce the dimension of data taking into account associations between attributes. Finally, we demonstrate our approach in an application to resting state functional magnetic resonance imaging (fMRI) data, which implies subtypes of depression may be characterized by the treatment effect of antidepressant drug SSRI.We propose a novel approach for the dimension reduction of high dimensional data to make the data available for conventional statistical evaluations. Our method is based on nonparametric multiple Gaussian clustering, in which we assume that in each cluster block, the instances follow an independent and identically (i.i.d.) univariate Gaussian distribution. We show theoretically that our model can fit multivariate Gaussian distributions with exchangeable features. We further show how the clusters derived with this specific model can be used to effectively reduce the dimension of data taking into account associations between attributes. Finally, we demonstrate our approach in an application to resting state functional magnetic resonance imaging (fMRI) data, which implies subtypes of depression may be characterized by the treatment effect of antidepressant drug SSRI.