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Exploration and analysis of scalar and multivariate volume data play an important role in different domains from physical simulations to medical treatments. In visual analytics of volumetric data sets we need to define a multidimenional transfer functions. This is a challenging task because of the difficulty in understanding multiple attribute spaces and displaying in physical three dimension space. Frequently, the transfer function is designed based on mapping one or two dimensional space to color properties (RGB) and opacity (A). We propose visualization and interaction methods for analyzing individual clusters as well as cluster distribution within and across levels in the cluster hierarchy. We also provide a clustering method that operates on density rather than individual records. We compute density in the given multidimensional multivariate space. Clusters are formed by areas of high density. We present an approach that automatically computes a hierarchical tree of high density clusters. To visually represent the cluster hierarchy, we present a 2D radial layout that supports an intuitive understanding of the distribution structure of the multidimensional multivariate data set. We apply a semi-automatic coloring scheme based on the 2D radial layout of the hierarchical cluster tree encoding hue, saturation, and value of the HSV color space. The system support an easily method to define the multidimensional transfer function over entire multidimensional attributes space. Finally, the surface extract by applying marching cube methods.