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Image feature separation is a crucial step for image segmentation in computer vision systems. One efficient and powerful approach is the unsupervised clustering of the resulting data set; however, it is a very computationally intensive task. This paper presents a high-performance architecture for unsupervised data clustering. This architecture is suitable for VLSI implementations. It exploits paradigms of massive connectivity like those inspired by neural networks, and parallelism and functionality integration that can be afforded by emerging nanometer semiconductor technologies. By utilizing a "global-quasi-systolic, local-hyper-connected" architectural approach, the hardware can process real-time DVD-quality video at the highest rate allowed by the MPEG-2 standard. The architecture is a realization of the histogram peak-climbing clustering algorithm, and it is the first special-purpose architecture that has been proposed for this important problem. The architecture has also been prototyped using a Xilinx field programmable gate array (FPGA) development environment. Although this paper discusses a computer vision application, the architecture presented can be utilized in the acceleration of the clustering process of any type of high-dimensionality data.